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1. Non-uniform seasonal warming regulates vegetation greening and atmospheric CO 2 amplification over northern lands

Author

Li, Z, Xia, J, Ahlström, A, Rinke, A, Koven, C, Hayes, DJ, Ji, D, Zhang, G, Krinner, G, Chen, G, Cheng, W, Dong, J, Liang, J, Moore, JC, Jiang, L, Yan, L, Ciais, P, Peng, S, Wang, YP, Xiao, X, Shi, Z, McGuire, AD, and Luo, Y

Subjects
MD Multidisciplinary, Meteorology & Atmospheric Sciences
Abstract

The enhanced vegetation growth by climate warming plays a pivotal role in amplifying the seasonal cycle of atmospheric CO 2 at northern lands (>50° N) since 1960s. However, the correlation between vegetation growth, temperature and seasonal amplitude of atmospheric CO 2 concentration have become elusive with the slowed increasing trend of vegetation growth and weakened temperature control on CO 2 uptake since late 1990s. Here, based on in situ atmospheric CO 2 concentration records from the Barrow observatory site, we found a slowdown in the increasing trend of the atmospheric CO 2 amplitude from 1990s to mid-2000s. This phenomenon was associated with the paused decrease in the minimum CO 2 concentration ([CO 2 ] min ), which was significantly correlated with the slowdown of vegetation greening and growing-season length extension. We then showed that both the vegetation greenness and growing-season length were positively correlated with spring but not autumn temperature over the northern lands. Furthermore, such asymmetric dependences of vegetation growth upon spring and autumn temperature cannot be captured by the state-of-art terrestrial biosphere models. These findings indicate that the responses of vegetation growth to spring and autumn warming are asymmetric, and highlight the need of improving autumn phenology in the models for predicting seasonal cycle of atmospheric CO 2 concentration.

Published
2018

2. ORCHIDEE-PEAT (revision 4596), a model for northern peatland CO2, water, and energy fluxes on daily to annual scales

Author

Qiu, C, Zhu, D, Ciais, P, Guenet, B, Krinner, G, Peng, S, Aurela, M, Bernhofer, C, Brümmer, C, Bret-Harte, S, Chu, H, Chen, J, Desai, AR, Dušek, J, Euskirchen, ES, Fortuniak, K, Flanagan, LB, Friborg, T, Grygoruk, M, Gogo, S, Grünwald, T, Hansen, BU, Holl, D, Humphreys, E, Hurkuck, M, Kiely, G, Klatt, J, Kutzbach, L, Largeron, C, Laggoun-Défarge, F, Lund, M, Lafleur, PM, Li, X, Mammarella, I, Merbold, L, Nilsson, MB, Olejnik, J, Ottosson-Löfvenius, M, Oechel, W, Parmentier, FJW, Peichl, M, Pirk, N, Peltola, O, Pawlak, W, Rasse, D, Rinne, J, Shaver, G, Peter Schmid, H, Sottocornola, M, Steinbrecher, R, Sachs, T, Urbaniak, M, Zona, D, and Ziemblinska, K

Subjects
Earth Sciences
Abstract

Peatlands store substantial amounts of carbon and are vulnerable to climate change. We present a modified version of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model for simulating the hydrology, surface energy, and CO2 fluxes of peatlands on daily to annual timescales. The model includes a separate soil tile in each 0.5° grid cell, defined from a global peatland map and identified with peat-specific soil hydraulic properties. Runoff from non-peat vegetation within a grid cell containing a fraction of peat is routed to this peat soil tile, which maintains shallow water tables. The water table position separates oxic from anoxic decomposition. The model was evaluated against eddy-covariance (EC) observations from 30 northern peatland sites, with the maximum rate of carboxylation (Vcmax) being optimized at each site. Regarding short-term day-to-day variations, the model performance was good for gross primary production (GPP) (r2 Combining double low line 0.76; Nash-Sutcliffe modeling efficiency, MEF Combining double low line 0.76) and ecosystem respiration (ER, r2 Combining double low line 0.78, MEF Combining double low line 0.75), with lesser accuracy for latent heat fluxes (LE, r2 Combining double low line 0.42, MEF Combining double low line 0.14) and and net ecosystem CO2 exchange (NEE, r2 Combining double low line 0.38, MEF Combining double low line 0.26). Seasonal variations in GPP, ER, NEE, and energy fluxes on monthly scales showed moderate to high r2 values (0.57-0.86). For spatial across-site gradients of annual mean GPP, ER, NEE, and LE, r2 values of 0.93, 0.89, 0.27, and 0.71 were achieved, respectively. Water table (WT) variation was not well predicted (r2

Published
2018

3. Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region

Author

Xia, J, McGuire, AD, Lawrence, D, Burke, E, Chen, G, Chen, X, Delire, C, Koven, C, MacDougall, A, Peng, S, Rinke, A, Saito, K, Zhang, W, Alkama, R, Bohn, TJ, Ciais, P, Decharme, B, Gouttevin, I, Hajima, T, Hayes, DJ, Huang, K, Ji, D, Krinner, G, Lettenmaier, DP, Miller, PA, Moore, JC, Smith, B, Sueyoshi, T, Shi, Z, Yan, L, Liang, J, Jiang, L, Zhang, Q, and Luo, Y

Subjects
Geophysics
Abstract

Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246 ± 6 g C m−2 yr−1), most models produced higher NPP (309 ± 12 g C m−2 yr−1) over the permafrost region during 2000–2009. By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982–2009, there was a twofold discrepancy among models (380 to 800 g C m−2 yr−1), which mainly resulted from differences in simulated maximum monthly GPP (GPPmax). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (Vcmax_25), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO2 concentration. These results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPPmax as well as their sensitivity to climate change.

Published
2017

4. Evaluation of air-soil temperature relationships simulated by land surface models during winter across the permafrost region

Author

Wang, W, Rinke, A, Moore, JC, Ji, D, Cui, X, Peng, S, Lawrence, DM, McGuire, AD, Burke, EJ, Chen, X, Decharme, B, Koven, C, MacDougall, A, Saito, K, Zhang, W, Alkama, R, Bohn, TJ, Ciais, P, Delire, C, Gouttevin, I, Hajima, T, Krinner, G, Lettenmaier, DP, Miller, PA, Smith, B, Sueyoshi, T, and Sherstiukov, AB

Subjects
Meteorology & Atmospheric Sciences, Oceanography, Physical Geography and Environmental Geoscience
Abstract

A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyse simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models, and compare them with observations from 268 Russian stations. There are large cross-model differences in the simulated differences between near-surface soil and air temperatures (ΔT; 3 to 14 °C), in the sensitivity of soil-to-air temperature (0.13 to 0.96 °C °C-1), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, hence guide improvements to the model's conceptual structure and process parameterisations. Models with better performance apply multilayer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (13.19 to 15.77 million km2). However, there is not a simple relationship between the sophistication of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, because several other factors, such as soil depth used in the models, the treatment of soil organic matter content, hydrology and vegetation cover, also affect the simulated permafrost distribution.

Published
2016

5. Variability in the sensitivity among model simulations of permafrost and carbon dynamics in the permafrost region between 1960 and 2009

Author

McGuire, AD, Koven, C, Lawrence, DM, Clein, JS, Xia, J, Beer, C, Burke, E, Chen, G, Chen, X, Delire, C, Jafarov, E, MacDougall, AH, Marchenko, S, Nicolsky, D, Peng, S, Rinke, A, Saito, K, Zhang, W, Alkama, R, Bohn, TJ, Ciais, P, Decharme, B, Ekici, A, Gouttevin, I, Hajima, T, Hayes, DJ, Ji, D, Krinner, G, Lettenmaier, DP, Luo, Y, Miller, PA, Moore, JC, Romanovsky, V, Schädel, C, Schaefer, K, Schuur, EAG, Smith, B, Sueyoshi, T, and Zhuang, Q

Subjects
carbon cycle, climate change, permafrost, permafrost carbon feedback, sensitivity, soil carbon, Meteorology & Atmospheric Sciences, Atmospheric Sciences, Geochemistry, Oceanography
Abstract

A significant portion of the large amount of carbon (C) currently stored in soils of the permafrost region in the Northern Hemisphere has the potential to be emitted as the greenhouse gases CO2 and CH4 under a warmer climate. In this study we evaluated the variability in the sensitivity of permafrost and C in recent decades among land surface model simulations over the permafrost region between 1960 and 2009. The 15 model simulations all predict a loss of near-surface permafrost (within 3 m) area over the region, but there are large differences in the magnitude of the simulated rates of loss among the models (0.2 to 58.8 × 103 km2 yr−1). Sensitivity simulations indicated that changes in air temperature largely explained changes in permafrost area, although interactions among changes in other environmental variables also played a role. All of the models indicate that both vegetation and soil C storage together have increased by 156 to 954 Tg C yr−1 between 1960 and 2009 over the permafrost region even though model analyses indicate that warming alone would decrease soil C storage. Increases in gross primary production (GPP) largely explain the simulated increases in vegetation and soil C. The sensitivity of GPP to increases in atmospheric CO2 was the dominant cause of increases in GPP across the models, but comparison of simulated GPP trends across the 1982–2009 period with that of a global GPP data set indicates that all of the models overestimate the trend in GPP. Disturbance also appears to be an important factor affecting C storage, as models that consider disturbance had lower increases in C storage than models that did not consider disturbance. To improve the modeling of C in the permafrost region, there is the need for the modeling community to standardize structural representation of permafrost and carbon dynamics among models that are used to evaluate the permafrost C feedback and for the modeling and observational communities to jointly develop data sets and methodologies to more effectively benchmark models.

Published
2016

6. Simulated high-latitude soil thermal dynamics during the past 4 decades

Author

Peng, S, Ciais, P, Krinner, G, Wang, T, Gouttevin, I, McGuire, AD, Lawrence, D, Burke, E, Chen, X, Decharme, B, Koven, C, MacDougall, A, Rinke, A, Saito, K, Zhang, W, Alkama, R, Bohn, TJ, Delire, C, Hajima, T, Ji, D, Lettenmaier, DP, Miller, PA, Moore, JC, Smith, B, and Sueyoshi, T

Subjects
Earth Sciences, Atmospheric Sciences, Climate Action, Oceanography, Physical Geography and Environmental Geoscience, Meteorology & Atmospheric Sciences, Physical geography and environmental geoscience
Abstract

Soil temperature (Ts/change is a key indicator of the dynamics of permafrost. On seasonal and interannual timescales, the variability of Ts determines the activelayer depth, which regulates hydrological soil properties and biogeochemical processes. On the multi-decadal scale, increasing Ts not only drives permafrost thaw/retreat but can also trigger and accelerate the decomposition of soil organic carbon. The magnitude of permafrost carbon feedbacks is thus closely linked to the rate of change of soil thermal regimes. In this study, we used nine process-based ecosystem models with permafrost processes, all forced by different observation-based climate forcing during the period 1960-2000, to characterize the warming rate of Ts in permafrost regions. There is a large spread of Ts trends at 20 cm depth across the models, with trend values ranging from 0.010 ± 0.003 to 0.031 ± 0.005 °C yr-1. Most models show smaller increase in Ts with increasing depth. Air temperature (Ta/and longwave downward radiation (LWDR) are the main drivers of Ts trends, but their relative contributions differ amongst the models. Different trends of LWDR used in the forcing of models can explain 61 % of their differences in Ts trends, while trends of Ta only explain 5 % of the differences in Ts trends. Uncertain climate forcing contributes a larger uncertainty in Ts trends (0.021 ± 0.008 °C yr-1, mean ± standard deviation) than the uncertainty of model structure (0.012 ± 0.001 °C yr-1/, diagnosed from the range of response between different models, normalized to the same forcing. In addition, the loss rate of near-surface permafrost area, defined as total area where the maximum seasonal active-layer thickness (ALT) is less than 3 m loss rate, is found to be significantly correlated with the magnitude of the trends of Ts at 1 m depth across the models (R D-0:85, P = 0:003), but not with the initial total nearsurface permafrost area (R =-0:30, P = 0:438). The sensitivity of the total boreal near-surface permafrost area to Ts at 1 m is estimated to be of-2.80 ± 0.67 million km2 °C-1. Finally, by using two long-term LWDR data sets and relationships between trends of LWDR and Ts across models, we infer an observation-constrained total boreal near-surface permafrost area decrease comprising between 39 ± 14 × 103 and 75 ± 14 × 103 km2 yr-1 from 1960 to 2000. This corresponds to 9-18 % degradation of the current permafrost area.

Published
2016

7. A simplified, data-constrained approach to estimate the permafrost carbonclimate feedback

Author

Koven, CD, Schuur, EAG, Schädel, C, Bohn, TJ, Burke, EJ, Chen, G, Chen, X, Ciais, P, Grosse, G, Harden, JW, Hayes, DJ, Hugelius, G, Jafarov, EE, Krinner, G, Kuhry, P, Lawrence, DM, MacDougall, AH, Marchenko, SS, McGuire, AD, Natali, SM, Nicolsky, DJ, Olefeldt, D, Peng, S, Romanovsky, VE, Schaefer, KM, Strauss, J, Treat, CC, and Turetsky, M

Subjects
Agricultural, Veterinary and Food Sciences, Biological Sciences, Forestry Sciences, Climate Action, Carbon, Climate Change, Computer Simulation, Databases, Factual, Ecosystem, Environmental Monitoring, Feedback, Freezing, Models, Chemical, Models, Statistical, Permafrost, permafrost, climate change, carbon-climate feedbacks, methane, carbon–climate feedbacks, General Science & Technology
Abstract

We present an approach to estimate the feedback from large-scale thawing of permafrost soils using a simplified, data-constrained model that combines three elements: soil carbon (C) maps and profiles to identify the distribution and type of C in permafrost soils; incubation experiments to quantify the rates of C lost after thaw; and models of soil thermal dynamics in response to climate warming. We call the approach the Permafrost Carbon Network Incubation-Panarctic Thermal scaling approach (PInc-PanTher). The approach assumes that C stocks do not decompose at all when frozen, but once thawed follow set decomposition trajectories as a function of soil temperature. The trajectories are determined according to a three-pool decomposition model fitted to incubation data using parameters specific to soil horizon types. We calculate litterfall C inputs required to maintain steady-state C balance for the current climate, and hold those inputs constant. Soil temperatures are taken from the soil thermal modules of ecosystem model simulations forced by a common set of future climate change anomalies under two warming scenarios over the period 2010 to 2100. Under a medium warming scenario (RCP4.5), the approach projects permafrost soil C losses of 12.2-33.4 Pg C; under a high warming scenario (RCP8.5), the approach projects C losses of 27.9-112.6 Pg C. Projected C losses are roughly linearly proportional to global temperature changes across the two scenarios. These results indicate a global sensitivity of frozen soil C to climate change (γ sensitivity) of -14 to -19 Pg C °C(-1) on a 100 year time scale. For CH4 emissions, our approach assumes a fixed saturated area and that increases in CH4 emissions are related to increased heterotrophic respiration in anoxic soil, yielding CH4 emission increases of 7% and 35% for the RCP4.5 and RCP8.5 scenarios, respectively, which add an additional greenhouse gas forcing of approximately 10-18%. The simplified approach presented here neglects many important processes that may amplify or mitigate C release from permafrost soils, but serves as a data-constrained estimate on the forced, large-scale permafrost C response to warming.

Published
2015

8. A simplified, data-constrained approach to estimate the permafrost carbon-climate feedback.

Author

Koven, CD, Schuur, EAG, Schädel, C, Bohn, TJ, Burke, EJ, Chen, G, Chen, X, Ciais, P, Grosse, G, Harden, JW, Hayes, DJ, Hugelius, G, Jafarov, EE, Krinner, G, Kuhry, P, Lawrence, DM, MacDougall, AH, Marchenko, SS, McGuire, AD, Natali, SM, Nicolsky, DJ, Olefeldt, D, Peng, S, Romanovsky, VE, Schaefer, KM, Strauss, J, Treat, CC, and Turetsky, M

Subjects
Carbon, Models, Statistical, Ecosystem, Environmental Monitoring, Freezing, Models, Chemical, Feedback, Computer Simulation, Databases, Factual, Climate Change, Permafrost, carbon–climate feedbacks, climate change, methane, permafrost, carbon-climate feedbacks, General Science & Technology
Abstract

We present an approach to estimate the feedback from large-scale thawing of permafrost soils using a simplified, data-constrained model that combines three elements: soil carbon (C) maps and profiles to identify the distribution and type of C in permafrost soils; incubation experiments to quantify the rates of C lost after thaw; and models of soil thermal dynamics in response to climate warming. We call the approach the Permafrost Carbon Network Incubation-Panarctic Thermal scaling approach (PInc-PanTher). The approach assumes that C stocks do not decompose at all when frozen, but once thawed follow set decomposition trajectories as a function of soil temperature. The trajectories are determined according to a three-pool decomposition model fitted to incubation data using parameters specific to soil horizon types. We calculate litterfall C inputs required to maintain steady-state C balance for the current climate, and hold those inputs constant. Soil temperatures are taken from the soil thermal modules of ecosystem model simulations forced by a common set of future climate change anomalies under two warming scenarios over the period 2010 to 2100. Under a medium warming scenario (RCP4.5), the approach projects permafrost soil C losses of 12.2-33.4 Pg C; under a high warming scenario (RCP8.5), the approach projects C losses of 27.9-112.6 Pg C. Projected C losses are roughly linearly proportional to global temperature changes across the two scenarios. These results indicate a global sensitivity of frozen soil C to climate change (γ sensitivity) of -14 to -19 Pg C °C(-1) on a 100 year time scale. For CH4 emissions, our approach assumes a fixed saturated area and that increases in CH4 emissions are related to increased heterotrophic respiration in anoxic soil, yielding CH4 emission increases of 7% and 35% for the RCP4.5 and RCP8.5 scenarios, respectively, which add an additional greenhouse gas forcing of approximately 10-18%. The simplified approach presented here neglects many important processes that may amplify or mitigate C release from permafrost soils, but serves as a data-constrained estimate on the forced, large-scale permafrost C response to warming.

Published
2015

9. Expert assessment of vulnerability of permafrost carbon to climate change

Author

Schuur, EAG, Abbott, BW, Bowden, WB, Brovkin, V, Camill, P, Canadell, JG, Chanton, JP, Chapin, FS, Christensen, TR, Ciais, P, Crosby, BT, Czimczik, CI, Grosse, G, Harden, J, Hayes, DJ, Hugelius, G, Jastrow, JD, Jones, JB, Kleinen, T, Koven, CD, Krinner, G, Kuhry, P, Lawrence, DM, McGuire, AD, Natali, SM, O’Donnell, JA, Ping, CL, Riley, WJ, Rinke, A, Romanovsky, VE, Sannel, ABK, Schädel, C, Schaefer, K, Sky, J, Subin, ZM, Tarnocai, C, Turetsky, MR, Waldrop, MP, Walter Anthony, KM, Wickland, KP, Wilson, CJ, and Zimov, SA

Subjects
Earth Sciences, Atmospheric Sciences, Environmental Sciences, Climate Action, Meteorology & Atmospheric Sciences
Abstract

Approximately 1700 Pg of soil carbon (C) are stored in the northern circumpolar permafrost zone, more than twice as much C than in the atmosphere. The overall amount, rate, and form of C released to the atmosphere in a warmer world will influence the strength of the permafrost C feedback to climate change. We used a survey to quantify variability in the perception of the vulnerability of permafrost C to climate change. Experts were asked to provide quantitative estimates of permafrost change in response to four scenarios of warming. For the highest warming scenario (RCP 8.5), experts hypothesized that C release from permafrost zone soils could be 19-45 Pg C by 2040, 162-288 Pg C by 2100, and 381-616 Pg C by 2300 in CO2 equivalent using 100-year CH4 global warming potential (GWP). These values become 50 % larger using 20-year CH4 GWP, with a third to a half of expected climate forcing coming from CH4 even though CH4 was only 2.3 % of the expected C release. Experts projected that two-thirds of this release could be avoided under the lowest warming scenario (RCP 2.6). These results highlight the potential risk from permafrost thaw and serve to frame a hypothesis about the magnitude of this feedback to climate change. However, the level of emissions proposed here are unlikely to overshadow the impact of fossil fuel burning, which will continue to be the main source of C emissions and climate forcing. © 2013 The Author(s).

Published
2013

10. GCOS EHI 1960-2020 Continental Heat Content (Version 2)

Author

Cuesta-Valero, Francisco Jose, Beltrami, H., García-García, Almudena, Krinner, G., Langer, M., MacDougall, A.H., Nitzbon, J., Peng, Jian ; orcid:0000-0002-4071-0512, von Schuckmann, K., Seneviratne, S.I., Thiery, W., Vanderkelen, I., Wu, T., Cuesta-Valero, Francisco Jose, Beltrami, H., García-García, Almudena, Krinner, G., Langer, M., MacDougall, A.H., Nitzbon, J., Peng, Jian ; orcid:0000-0002-4071-0512, von Schuckmann, K., Seneviratne, S.I., Thiery, W., Vanderkelen, I., and Wu, T.

Abstract

The Earth climate system is out of energy balance, and heat has accumulated continuously over the past decades, warming the ocean, the land, the cryosphere, and the atmosphere. According to the Sixth Assessment Report by Working Group I of the Intergovernmental Panel on Climate Change, this planetary warming over multiple decades is human-driven and results in unprecedented and committed changes to the Earth system, with adverse impacts for ecosystems and human systems. The Earth heat inventory provides a measure of the Earth energy imbalance (EEI) and allows for quantifying how much heat has accumulated in the Earth system, as well as where the heat is stored. Here we show that the Earth system has continued to accumulate heat, with 381±61 ZJ accumulated from 1971 to 2020. This is equivalent to a heating rate (i.e., the EEI) of 0.48±0.1 W m−2. The majority, about 89 %, of this heat is stored in the ocean, followed by about 6 % on land, 1 % in the atmosphere, and about 4 % available for melting the cryosphere. Over the most recent period (2006–2020), the EEI amounts to 0.76±0.2 W m−2. The Earth energy imbalance is the most fundamental global climate indicator that the scientific community and the public can use as the measure of how well the world is doing in the task of bringing anthropogenic climate change under control. Moreover, this indicator is highly complementary to other established ones like global mean surface temperature as it represents a robust measure of the rate of climate change and its future commitment. We call for an implementation of the Earth energy imbalance into the Paris Agreement's Global Stocktake based on best available science. The Earth heat inventory in this study, updated from von Schuckmann et al. (2020), is underpinned by worldwide multidisciplinary collaboration and demonstrates the critical importance of concerted international efforts for climate change monitoring and community-based recommendations and we also

Published
2023

11. GCOS EHI 1960-2020 Earth Heat Inventory Ocean Heat Content (Version 2)

Author

von Schuckmann, K., Minière, A., Gues, F., Cuesta-Valero, Francisco Jose, Kirchengast, G., Adusumilli, S., Straneo, F., Allan, R.P., Barker, P.M., Beltrami, H., Blazquez, A., Boyer, T., Cheng, L., Church, J., Desbruyeres, D., Dolman, H., Domingues, C.M., García-García, Almudena, Gilson, J.E., Gorfer, M., Haimberger, L., Hendricks, S., Hosoda, S., Johnson, G.C., Killick, R., King, B., Kolodziejczyk, N., Korosov, A., Krinner, G., Kuusela, M., Langer, M., Lavergne, T., Lawrence, I., Li, Y., Lyman, J., Marti, F., Marzeion, B., Mayer, M., MacDougall, A.H., McDougall, T., Monselesan, D.P., Nitzbon, J., Otosaka, I., Peng, Jian ; orcid:0000-0002-4071-0512, Purkey, S., Roemmich, D., Sato, K., Savita, A., Schweiger, A., Shepherd, A., Seneviratne, S.I., Slater, D.A., Slater, T., Simons, L., Steiner, A.K., Szekely, T., Suga, T., Thiery, W., Timmermans, M.-L., Vanderkelen, I., Wjiffels, S.E., Wu, T., Zemp, M., von Schuckmann, K., Minière, A., Gues, F., Cuesta-Valero, Francisco Jose, Kirchengast, G., Adusumilli, S., Straneo, F., Allan, R.P., Barker, P.M., Beltrami, H., Blazquez, A., Boyer, T., Cheng, L., Church, J., Desbruyeres, D., Dolman, H., Domingues, C.M., García-García, Almudena, Gilson, J.E., Gorfer, M., Haimberger, L., Hendricks, S., Hosoda, S., Johnson, G.C., Killick, R., King, B., Kolodziejczyk, N., Korosov, A., Krinner, G., Kuusela, M., Langer, M., Lavergne, T., Lawrence, I., Li, Y., Lyman, J., Marti, F., Marzeion, B., Mayer, M., MacDougall, A.H., McDougall, T., Monselesan, D.P., Nitzbon, J., Otosaka, I., Peng, Jian ; orcid:0000-0002-4071-0512, Purkey, S., Roemmich, D., Sato, K., Savita, A., Schweiger, A., Shepherd, A., Seneviratne, S.I., Slater, D.A., Slater, T., Simons, L., Steiner, A.K., Szekely, T., Suga, T., Thiery, W., Timmermans, M.-L., Vanderkelen, I., Wjiffels, S.E., Wu, T., and Zemp, M.

Abstract

The Earth climate system is out of energy balance, and heat has accumulated continuously over the past decades, warming the ocean, the land, the cryosphere, and the atmosphere. According to the Sixth Assessment Report by Working Group I of the Intergovernmental Panel on Climate Change, this planetary warming over multiple decades is human-driven and results in unprecedented and committed changes to the Earth system, with adverse impacts for ecosystems and human systems. The Earth heat inventory provides a measure of the Earth energy imbalance (EEI) and allows for quantifying how much heat has accumulated in the Earth system, as well as where the heat is stored. Here we show that the Earth system has continued to accumulate heat, with 381±61 ZJ accumulated from 1971 to 2020. This is equivalent to a heating rate (i.e., the EEI) of 0.48±0.1 W m−2. The majority, about 89 %, of this heat is stored in the ocean, followed by about 6 % on land, 1 % in the atmosphere, and about 4 % available for melting the cryosphere. Over the most recent period (2006–2020), the EEI amounts to 0.76±0.2 W m−2. The Earth energy imbalance is the most fundamental global climate indicator that the scientific community and the public can use as the measure of how well the world is doing in the task of bringing anthropogenic climate change under control. Moreover, this indicator is highly complementary to other established ones like global mean surface temperature as it represents a robust measure of the rate of climate change and its future commitment. We call for an implementation of the Earth energy imbalance into the Paris Agreement's Global Stocktake based on best available science. The Earth heat inventory in this study, updated from von Schuckmann et al. (2020), is underpinned by worldwide multidisciplinary collaboration and demonstrates the critical importance of concerted international efforts for climate change monitoring and community-based recommendations and we also

Published
2023

12. Communicating future sea-level rise uncertainty and ambiguity to assessment users

Author

Kopp, R.E., Oppenheimer, M., O’Reilly, J.L., Drijfhout, S., Edwards, T.L., Fox-Kemper, B., Garner, G.G., Golledge, N.R., Hermans, T.H.J., Hewitt, H.T., Horton, B.P., Krinner, G., Notz, D., Nowicki, S., Palmer, M.D., Slangen, A.B.A., Xiao, C., Kopp, R.E., Oppenheimer, M., O’Reilly, J.L., Drijfhout, S., Edwards, T.L., Fox-Kemper, B., Garner, G.G., Golledge, N.R., Hermans, T.H.J., Hewitt, H.T., Horton, B.P., Krinner, G., Notz, D., Nowicki, S., Palmer, M.D., Slangen, A.B.A., and Xiao, C.

Abstract

Future sea-level change is characterized by both quantifiable and unquantifiable uncertainties. Effective communication of both types of uncertainty is a key challenge in translating sea-level science to inform long-term coastal planning. Scientific assessments play a key role in the translation process and have taken diverse approaches to communicating sea-level projection uncertainty. Here we review how past IPCC and regional assessments have presented sea-level projection uncertainty, how IPCC presentations have been interpreted by regional assessments and how regional assessments and policy guidance simplify projections for practical use. This information influenced the IPCC Sixth Assessment Report presentation of quantifiable and unquantifiable uncertainty, with the goal of preserving both elements as projections are adapted for regional application

Published
2023

13. Continental heat storage: contributions from the ground, inland waters, and permafrost thawing

Author

Cuesta-Valero, Francisco Jose, Beltrami, H., García-García, Almudena, Krinner, G., Langer, M., MacDougall, A.H., Nitzbon, J., Peng, Jian, von Schuckmann, K., Seneviratne, S.I., Thiery, W., Vanderkelen, I., Wu, T., Cuesta-Valero, Francisco Jose, Beltrami, H., García-García, Almudena, Krinner, G., Langer, M., MacDougall, A.H., Nitzbon, J., Peng, Jian, von Schuckmann, K., Seneviratne, S.I., Thiery, W., Vanderkelen, I., and Wu, T.

Abstract

Heat storage within the Earth system is a fundamental metric for understanding climate change. The current energy imbalance at the top of the atmosphere causes changes in energy storage within the ocean, the atmosphere, the cryosphere, and the continental landmasses. After the ocean, heat storage in land is the second largest term of the Earth heat inventory, affecting physical processes relevant to society and ecosystems, such as the stability of the soil carbon pool. Here, we present an update of the continental heat storage, combining for the first time the heat in the land subsurface, inland water bodies, and permafrost thawing. The continental landmasses stored 23.8 ± 2.0 × 1021 J during the period 1960–2020, but the distribution of heat among the three components is not homogeneous. The sensible diffusion of heat through the ground accounts for ∼90 % of the continental heat storage, with inland water bodies and permafrost degradation (i.e. latent heat) accounting for ∼0.7 % and ∼9 % of the continental heat, respectively. Although the inland water bodies and permafrost soils store less heat than the solid ground, we argue that their associated climate phenomena justify their monitoring and inclusion in the Earth heat inventory.

Published
2023

14. Heat stored in the Earth system 1960–2020: where does the energy go?

Author

von Schuckmann, K., Minière, A., Gues, F., Cuesta-Valero, Francisco Jose, Kirchengast, G., Adusumilli, S., Straneo, F., Ablain, M., Allan, R.P., Barker, P.M., Beltrami, H., Blazquez, A., Boyer, T., Cheng, L., Church, J., Desbruyeres, D., Dolman, H., Domingues, C.M., García-García, Almudena, Giglio, D., Gilson, J.E., Gorfer, M., Haimberger, L., Hakuba, M.Z., Hendricks, S., Hosoda, S., Johnson, G.C., Killick, R., King, B., Kolodziejczyk, N., Korosov, A., Krinner, G., Kuusela, M., Landerer, F.W., Langer, M., Lavergne, T., Lawrence, I., Li, Y., Lyman, J., Marti, F., Marzeion, B., Mayer, M., MacDougall, A.H., McDougall, T., Monselesan, D.P., Nitzbon, J., Otosaka, I., Peng, Jian, Purkey, S., Roemmich, D., Sato, K., Savita, A., Schweiger, A., Shepherd, A., Seneviratne, S.I., Simons, L., Slater, D.A., Slater, T., Steiner, A.K., Suga, T., Szekely, T., Thiery, W., Timmermans, M.-L., Vanderkelen, I., Wjiffels, S.E., Wu, T., Zemp, M., von Schuckmann, K., Minière, A., Gues, F., Cuesta-Valero, Francisco Jose, Kirchengast, G., Adusumilli, S., Straneo, F., Ablain, M., Allan, R.P., Barker, P.M., Beltrami, H., Blazquez, A., Boyer, T., Cheng, L., Church, J., Desbruyeres, D., Dolman, H., Domingues, C.M., García-García, Almudena, Giglio, D., Gilson, J.E., Gorfer, M., Haimberger, L., Hakuba, M.Z., Hendricks, S., Hosoda, S., Johnson, G.C., Killick, R., King, B., Kolodziejczyk, N., Korosov, A., Krinner, G., Kuusela, M., Landerer, F.W., Langer, M., Lavergne, T., Lawrence, I., Li, Y., Lyman, J., Marti, F., Marzeion, B., Mayer, M., MacDougall, A.H., McDougall, T., Monselesan, D.P., Nitzbon, J., Otosaka, I., Peng, Jian, Purkey, S., Roemmich, D., Sato, K., Savita, A., Schweiger, A., Shepherd, A., Seneviratne, S.I., Simons, L., Slater, D.A., Slater, T., Steiner, A.K., Suga, T., Szekely, T., Thiery, W., Timmermans, M.-L., Vanderkelen, I., Wjiffels, S.E., Wu, T., and Zemp, M.

Abstract

The Earth climate system is out of energy balance, and heat has accumulated continuously over the past decades, warming the ocean, the land, the cryosphere, and the atmosphere. According to the Sixth Assessment Report by Working Group I of the Intergovernmental Panel on Climate Change, this planetary warming over multiple decades is human-driven and results in unprecedented and committed changes to the Earth system, with adverse impacts for ecosystems and human systems. The Earth heat inventory provides a measure of the Earth energy imbalance (EEI) and allows for quantifying how much heat has accumulated in the Earth system, as well as where the heat is stored. Here we show that the Earth system has continued to accumulate heat, with 381±61 ZJ accumulated from 1971 to 2020. This is equivalent to a heating rate (i.e., the EEI) of 0.48±0.1 W m−2. The majority, about 89 %, of this heat is stored in the ocean, followed by about 6 % on land, 1 % in the atmosphere, and about 4 % available for melting the cryosphere. Over the most recent period (2006–2020), the EEI amounts to 0.76±0.2 W m−2. The Earth energy imbalance is the most fundamental global climate indicator that the scientific community and the public can use as the measure of how well the world is doing in the task of bringing anthropogenic climate change under control. Moreover, this indicator is highly complementary to other established ones like global mean surface temperature as it represents a robust measure of the rate of climate change and its future commitment. We call for an implementation of the Earth energy imbalance into the Paris Agreement's Global Stocktake based on best available science. The Earth heat inventory in this study, updated from von Schuckmann et al. (2020), is underpinned by worldwide multidisciplinary collaboration and demonstrates the critical importance of concerted international efforts for climate change monitoring and community-based recommendations and we also call for urgently

Published
2023

15. Transient simulations of the Mid-Holocene with MGV, the IPSL fast ocean-atmosphere-vegetation general circulation model

Author

Gential, L., Ringeval, B., Sepulchre, P., Krinner, G., Ramstein, G., Friedlingstein, P., Laboratoire des Sciences du Climat et de l'Environnement (LSCE), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)

Subjects
[SDU]Sciences of the Universe [physics]
Abstract

International audience; A fast OAVGCM called MGV has been developed at IPSL to handle multi-millennial climate simulations. It couples the ocean and sea-ice OPA-LIM model to the atmosphere-vegetation LMDZ-ORCHIDEE model. The more affordable computing cost compared to the version of the IPSL Climate Model used for the IPCC has been obtained by decreasing the resolution of the atmosphere and vegetation components down to a mesh of 44 per 43 grid points in the horizontal. In ORCHIDEE, the carbon cycle is activated (STOMATE) as well as the dynamic vegetation (based on LPJ). On the other hand, the ocean carbon cycle and atmospheric chemistry are not taken into account yet due to their prohibitive additional computing costs. Two transient runs of the middle Holocene are carried out with the newly designed model. In one of them, the dynamic vegetation is not activated to assess this biospheric feedback on climate. The transient response of the climate system to solar forcings and gas concentrations is analysed with emphasize on changes in vegetation cover (such as the greening of the Sahara) and carbon fluxes. Lake extend (such as Lake Chad) and emissions of methane by wetlands are diagnosed using offline simulations with a refined version of ORCHIDEE recently developed at LSCE.

Published
2023

16. A simplified, data-constrained approach to estimate the permafrost carbon–climate feedback

Author

Koven, C. D., Schuur, E. A. G., Schädel, C., Bohn, T. J., Burke, E. J., Chen, G., Chen, X., Ciais, P., Grosse, G., Harden, J. W., Hayes, D. J., Hugelius, G., Jafarov, E. E., Krinner, G., Kuhry, P., Lawrence, D. M., MacDougall, A. H., Marchenko, S. S., McGuire, A. D., Natali, S. M., Nicolsky, D. J., Olefeldt, D., Peng, S., Romanovsky, V. E., Schaefer, K. M., Strauss, J., Treat, C. C., and Turetsky, M.

Published
2015

17. GCOS EHI 1960-2020 Earth Heat Inventory Ocean Heat Content (Version 1)

Author

von Schuckmann, K., Minière, A., Gues, F., Cuesta-Valero, Francisco Jose, Kirchengast, G., Adusumilli, S., Straneo, F., Allan, R.P., Barker, P.M., Beltrami, H., Blazquez, A., Boyer, T., Cheng, L., Church, J., Desbruyeres, D., Dolman, H., Domingues, C.M., García-García, Almudena, Gilson, J.E., Gorfer, M., Haimberger, L., Hendricks, S., Hosoda, S., Johnson, G.C., Killick, R., King, B., Kolodziejczyk, N., Korosov, A., Krinner, G., Kuusela, M., Langer, M., Lavergne, T., Lawrence, I., Li, Y., Lyman, J., Marti, F., Marzeion, B., Mayer, M., MacDougall, A.H., McDougall, T., Monselesan, D.P., Nitzbon, J., Otosaka, I., Peng, Jian ; orcid:0000-0002-4071-0512, Purkey, S., Roemmich, D., Sato, K., Savita, A., Schweiger, A., Shepherd, A., Seneviratne, S.I., Slater, D.A., Slater, T., Simons, L., Steiner, A.K., Szekely, T., Suga, T., Thiery, W., Timmermans, M.-L., Vanderkelen, I., Wjiffels, S.E., Wu, T., Zemp, M., von Schuckmann, K., Minière, A., Gues, F., Cuesta-Valero, Francisco Jose, Kirchengast, G., Adusumilli, S., Straneo, F., Allan, R.P., Barker, P.M., Beltrami, H., Blazquez, A., Boyer, T., Cheng, L., Church, J., Desbruyeres, D., Dolman, H., Domingues, C.M., García-García, Almudena, Gilson, J.E., Gorfer, M., Haimberger, L., Hendricks, S., Hosoda, S., Johnson, G.C., Killick, R., King, B., Kolodziejczyk, N., Korosov, A., Krinner, G., Kuusela, M., Langer, M., Lavergne, T., Lawrence, I., Li, Y., Lyman, J., Marti, F., Marzeion, B., Mayer, M., MacDougall, A.H., McDougall, T., Monselesan, D.P., Nitzbon, J., Otosaka, I., Peng, Jian ; orcid:0000-0002-4071-0512, Purkey, S., Roemmich, D., Sato, K., Savita, A., Schweiger, A., Shepherd, A., Seneviratne, S.I., Slater, D.A., Slater, T., Simons, L., Steiner, A.K., Szekely, T., Suga, T., Thiery, W., Timmermans, M.-L., Vanderkelen, I., Wjiffels, S.E., Wu, T., and Zemp, M.

Abstract

The Earth climate system is out of energy balance, and heat has accumulated continuously over the past decades, warming the ocean, the land, the cryosphere, and the atmosphere. According to the Sixth Assessment Report by Working Group I of the Intergovernmental Panel on Climate Change, this planetary warming over multiple decades is human-driven and results in unprecedented and committed changes to the Earth system, with adverse impacts for ecosystems and human systems. The Earth heat inventory provides a measure of the Earth energy imbalance (EEI) and allows for quantifying how much heat has accumulated in the Earth system, as well as where the heat is stored. Here we show that the Earth system has continued to accumulate heat, with 381±61 ZJ accumulated from 1971 to 2020. This is equivalent to a heating rate (i.e., the EEI) of 0.48±0.1 W m−2. The majority, about 89 %, of this heat is stored in the ocean, followed by about 6 % on land, 1 % in the atmosphere, and about 4 % available for melting the cryosphere. Over the most recent period (2006–2020), the EEI amounts to 0.76±0.2 W m−2. The Earth energy imbalance is the most fundamental global climate indicator that the scientific community and the public can use as the measure of how well the world is doing in the task of bringing anthropogenic climate change under control. Moreover, this indicator is highly complementary to other established ones like global mean surface temperature as it represents a robust measure of the rate of climate change and its future commitment. We call for an implementation of the Earth energy imbalance into the Paris Agreement's Global Stocktake based on best available science. The Earth heat inventory in this study, updated from von Schuckmann et al. (2020), is underpinned by worldwide multidisciplinary collaboration and demonstrates the critical importance of concerted international efforts for climate change monitoring and community-based recommendations and we also

Published
2022

18. GCOS EHI 1960-2020 Continental Heat Content (Version 1)

Author

Cuesta-Valero, Francisco Jose, Beltrami, H., García-García, Almudena, Krinner, G., Langer, M., MacDougall, A.H., Nitzbon, J., Peng, Jian ; orcid:0000-0002-4071-0512, von Schuckmann, K., Seneviratne, S.I., Smith, N., Thiery, W., Vanderkelen, I., Wu, T., Cuesta-Valero, Francisco Jose, Beltrami, H., García-García, Almudena, Krinner, G., Langer, M., MacDougall, A.H., Nitzbon, J., Peng, Jian ; orcid:0000-0002-4071-0512, von Schuckmann, K., Seneviratne, S.I., Smith, N., Thiery, W., Vanderkelen, I., and Wu, T.

Abstract

The Earth climate system is out of energy balance, and heat has accumulated continuously over the past decades, warming the ocean, the land, the cryosphere, and the atmosphere. According to the Sixth Assessment Report by Working Group I of the Intergovernmental Panel on Climate Change, this planetary warming over multiple decades is human-driven and results in unprecedented and committed changes to the Earth system, with adverse impacts for ecosystems and human systems. The Earth heat inventory provides a measure of the Earth energy imbalance (EEI) and allows for quantifying how much heat has accumulated in the Earth system, as well as where the heat is stored. Here we show that the Earth system has continued to accumulate heat, with 381±61 ZJ accumulated from 1971 to 2020. This is equivalent to a heating rate (i.e., the EEI) of 0.48±0.1 W m−2. The majority, about 89 %, of this heat is stored in the ocean, followed by about 6 % on land, 1 % in the atmosphere, and about 4 % available for melting the cryosphere. Over the most recent period (2006–2020), the EEI amounts to 0.76±0.2 W m−2. The Earth energy imbalance is the most fundamental global climate indicator that the scientific community and the public can use as the measure of how well the world is doing in the task of bringing anthropogenic climate change under control. Moreover, this indicator is highly complementary to other established ones like global mean surface temperature as it represents a robust measure of the rate of climate change and its future commitment. We call for an implementation of the Earth energy imbalance into the Paris Agreement's Global Stocktake based on best available science. The Earth heat inventory in this study, updated from von Schuckmann et al. (2020), is underpinned by worldwide multidisciplinary collaboration and demonstrates the critical importance of concerted international efforts for climate change monitoring and community-based recommendations and we also

Published
2022

22. Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5

Author

Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont, O., Balkanski, Y., Bekki, S., Bellenger, H., Benshila, R., Bony, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P., Cheruy, F., Codron, F., Cozic, A., Cugnet, D., de Noblet, N., Duvel, J.-P., Ethé, C., Fairhead, L., Fichefet, T., Flavoni, S., Friedlingstein, P., Grandpeix, J.-Y., Guez, L., Guilyardi, E., Hauglustaine, D., Hourdin, F., Idelkadi, A., Ghattas, J., Joussaume, S., Kageyama, M., Krinner, G., Labetoulle, S., Lahellec, A., Lefebvre, M.-P., Lefevre, F., Levy, C., Li, Z. X., Lloyd, J., Lott, F., Madec, G., Mancip, M., Marchand, M., Masson, S., Meurdesoif, Y., Mignot, J., Musat, I., Parouty, S., Polcher, J., Rio, C., Schulz, M., Swingedouw, D., Szopa, S., Talandier, C., Terray, P., Viovy, N., and Vuichard, N.

Published
2013
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23. Key features of the IPSL ocean atmosphere model and its sensitivity to atmospheric resolution

Author

Marti, Olivier, Braconnot, P., Dufresne, J.-L., Bellier, J., Benshila, R., Bony, S., Brockmann, P., Cadule, P., Caubel, A., Codron, F., de Noblet, N., Denvil, S., Fairhead, L., Fichefet, T., Foujols, M.-A., Friedlingstein, P., Goosse, H., Grandpeix, J.-Y., Guilyardi, E., Hourdin, F., Idelkadi, A., Kageyama, M., Krinner, G., Lévy, C., Madec, G., Mignot, J., Musat, I., Swingedouw, D., and Talandier, C.

Published
2010
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24. Ocean, cryosphere, and sea level change

Author

Fox-Kemper, B., Hewitt, H., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S., Edwards, T., Golledge, N., Hemer, M., Kopp, R., Krinner, G., Mix, A., Notz, D., Nowicki , S., Nurhati, I., Ruiz, L., Sallée, J., Slangen, A., and Yu, Y.

Published
2021

25. 2021: Ocean, cryosphere and sea level change

Author

Fox-Kemper, B., Hewitt, H.T., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S.S., Edwards, T.L., Golledge, N.R., Hemer, M., Kopp, R.E., Krinner, G., Mix, A., Notz, D., Nowicki, S., Nurhati, I.S., Ruiz, L., Sallée, J.-B., Slangen, A.B.A., and Yu, Y.

Abstract

This chapter assesses past and projected changes in the ocean, cryosphere and sea level using paleo reconstructions, instrumental observations and model simulations. In the following summary, we update and expand the related assessments from the IPCC Fifth Assessment Report (AR5), the Special Report on Global Warming of 1.5ºC (SR1.5) and the Special Report on Ocean and Cryosphere in a Changing Climate (SROCC). Major advances in this chapter since the SROCC include the synthesis of extended and new observations, which allows for improved assessment of past change, processes and budgets for the last century, and the use of a hierarchy of models and emulators, which provide improved projections and uncertainty estimates of future change. In addition, the systematic use of model emulators makes our projections of ocean heat content, land-ice loss and sea level rise fully consistent both with each other and with the assessed equilibrium climate sensitivity and projections of global surface air temperature across the entire report. In this executive summary, uncertainty ranges are reported as very likely ranges and expressed by square brackets, unless otherwise noted.

Published
2021

28. Past and future polar amplification of climate change: climate model intercomparisons and ice-core constraints

Author

Masson-Delmotte, V., Kageyama, M., Braconnot, P., Charbit, S., Krinner, G., Ritz, C., Guilyardi, E., Jouzel, J., Abe-Ouchi, A., Crucifix, M., Gladstone, R. M., Hewitt, C. D., Kitoh, A., LeGrande, A. N., Marti, O., Merkel, U., Motoi, T., Ohgaito, R., Otto-Bliesner, B., Peltier, W. R., Ross, I., Valdes, P. J., Vettoretti, G., Weber, S. L., Wolk, F., and Yu, Y.

Published
2006
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30. Europe-wide reduction in primary productivity caused by the heat and drought in 2003

Author

Ciais, Ph., Reichstein, M., Viovy, N., Granier, A., Ogee, J., Allard, V., Aubinet, M., Buchmann, N., Bernhofer, Chr., Carrara, A., Chevallier, F., De Noblet, N., Friend, A. D., Friedlingstein, P., Grunwald, T., Heinesch, B., Keronen, P., Knohl, A., Krinner, G., Loustau, D., Manca, G., Matteucci, G., Miglietta, F., Ourcival, J. M., Papale, D., Pilegaard, K., Rambal, S., Seufert, G., Soussana, J. F., Sanz, M. J., Schulze, E. D., Vesala, T., and Valentini, R.

Abstract

Author(s): Ph. Ciais (corresponding author) [1]; M. Reichstein [2, 3]; N. Viovy [1]; A. Granier [4]; J. Ogée [5]; V. Allard [6]; M. Aubinet [7]; N. Buchmann [8]; Chr. Bernhofer [...]

Published
2005
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35. Corrigendum to 'Impact of CO2 and climate on the Last Glacial Maximum vegetation: results from the ORCHIDEE/IPSL models' published in Clim. Past, 7, 557–577, 2011

Author

Woillez, M.-N., Kageyama, M., Krinner, G., De Noblet-Ducoudré, N., Viovy, N., Mancip, M., Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Modélisation du climat (CLIM), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire de glaciologie et géophysique de l'environnement (LGGE), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Modélisation des Surfaces et Interfaces Continentales (MOSAIC), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, lcsh:GE1-350, lcsh:Environmental pollution, lcsh:Environmental protection, lcsh:TD172-193.5, lcsh:TD169-171.8, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, ComputingMilieux_MISCELLANEOUS, lcsh:Environmental sciences
Abstract

International audience

Published
2018
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36. Assessment of model estimates of land-atmosphere CO2 exchange across Northern Eurasia

Author

Rawlins, M. A., McGuire, A. D., Kimball, J. S., Dass, P., Lawrence, D., Burke, E., Chen, X., Delire, C., Koven, C., MacDougall, A., Peng, S., Rinke, A., Saito, K., Zhang, W., Alkama, R., Bohn, T. J., Ciais, P., Decharme, B., Gouttevin, I., Hajima, T., Ji, D., Krinner, G., Lettenmaier, D. P., Miller, P., Moore, J. C., Smith, B., and Sueyoshi, T.

Subjects
lcsh:Geology, lcsh:QH501-531, lcsh:QH540-549.5, lcsh:QE1-996.5, lcsh:Life, lcsh:Ecology
Abstract

A warming climate is altering land-atmosphere exchanges of carbon, with a potential for increased vegetation productivity as well as the mobilization of permafrost soil carbon stores. Here we investigate land-atmosphere carbon dioxide (CO2) cycling through analysis of net ecosystem productivity (NEP) and its component fluxes of gross primary productivity (GPP) and ecosystem respiration (ER) and soil carbon residence time, simulated by a set of land surface models (LSMs) over a region spanning the drainage basin of Northern Eurasia. The retrospective simulations cover the period 1960–2009 at 0.5° resolution, which is a scale common among many global carbon and climate model simulations. Model performance benchmarks were drawn from comparisons against both observed CO2 fluxes derived from site-based eddy covariance measurements as well as regional-scale GPP estimates based on satellite remote-sensing data. The site-based comparisons depict a tendency for overestimates in GPP and ER for several of the models, particularly at the two sites to the south. For several models the spatial pattern in GPP explains less than half the variance in the MODIS MOD17 GPP product. Across the models NEP increases by as little as 0.01 to as much as 0.79 g C m−2 yr−2, equivalent to 3 to 340 % of the respective model means, over the analysis period. For the multimodel average the increase is 135 % of the mean from the first to last 10 years of record (1960–1969 vs. 2000–2009), with a weakening CO2 sink over the latter decades. Vegetation net primary productivity increased by 8 to 30 % from the first to last 10 years, contributing to soil carbon storage gains. The range in regional mean NEP among the group is twice the multimodel mean, indicative of the uncertainty in CO2 sink strength. The models simulate that inputs to the soil carbon pool exceeded losses, resulting in a net soil carbon gain amid a decrease in residence time. Our analysis points to improvements in model elements controlling vegetation productivity and soil respiration as being needed for reducing uncertainty in land-atmosphere CO2 exchange. These advances will require collection of new field data on vegetation and soil dynamics, the development of benchmarking data sets from measurements and remote-sensing observations, and investments in future model development and intercomparison studies.

Published
2015

37. A New Process-Based Soil Methane Scheme:Evaluation Over Arctic Field Sites With the ISBA Land Surface Model

Author

Morel, X., Decharme, B., Delire, C., Krinner, G., Lund, M., Hansen, B. U., Mastepanov, M., Morel, X., Decharme, B., Delire, C., Krinner, G., Lund, M., Hansen, B. U., and Mastepanov, M.

Abstract

Permafrost soils and arctic wetlands methane emissions represent an important challenge for modeling the future climate. Here we present a process-based model designed to correctly represent the main thermal, hydrological, and biogeochemical processes related to these emissions for general land surface modeling. We propose a new multilayer soil carbon and gas module within the Interaction Soil-Biosphere-Atmosphere (ISBA) land-surface model (LSM). This module represents carbon pools, vertical carbon dynamics, and both oxic and anoxic organic matter decomposition. It also represents the soil gas processes for CH4, CO2, and O2 through the soil column. We base CH4 production and oxydation on an O2 control instead of the classical water table level strata approach used in state-of-the-art soil CH4 models. We propose a new parametrization of CH4 oxydation using recent field experiments and use an explicit O2 limitation for soil carbon decomposition. Soil gas transport is computed explicitly, using a revisited formulation of plant-mediated transport, a new representation of gas bulk diffusivity in porous media closer to experimental observations, and an innovative advection term for ebullition. We evaluate this advanced model on three climatically distinct sites : two in Greenland (Nuuk and Zackenberg) and one in Siberia (Chokurdakh). The model realistically reproduces methane and carbon dioxide emissions from both permafrosted and nonpermafrosted sites. The evolution and vertical characteristics of the underground processes leading to these fluxes are consistent with current knowledge. Results also show that physics is the main driver of methane fluxes, and the main source of variability appears to be the water table depth.

Published
2019

38. The weakening relationship between Eurasian spring snow cover and Indian summer monsoon rainfall

Author

Zhang, T., Wang, T., Krinner, G., Wang, X., Gasser, T., Peng, S., Piao, S., Yao, T., Zhang, T., Wang, T., Krinner, G., Wang, X., Gasser, T., Peng, S., Piao, S., and Yao, T.

Abstract

Substantial progress has been made in understanding how Eurasian snow cover variabilities affect the Indian summer monsoon, but the snow-monsoon relationship in a warming atmosphere remains controversial. Using long-term observational snow and rainfall data (1967–2015), we identified that the widely recognized inverse relationship of central Eurasian spring snow cover with the Indian summer monsoon rainfall has disappeared since 1990. The apparent loss of this negative correlation is mainly due to the central Eurasian spring snow cover no longer regulating the summer mid-tropospheric temperature over the Iranian Plateau and surroundings, and hence the land-ocean thermal contrast after 1990. A reduced lagged snow-hydrological effect, resulting from a warming-induced decline in spring snow cover, constitutes the possible mechanism for the breakdown of the snow-air temperature connection after 1990. Our results suggest that, in a changing climate, Eurasian spring snow cover may not be a faithful predictor of the Indian summer monsoon rainfall.

Published
2019

39. ORCHIDEE-PEAT (revision 4596), a model for northern peatland CO2, water, and energy fluxes on daily to annual scales

Author

Qiu, C., Zhu, D., Ciais, P., Guenet, B., Krinner, G., Peng, S., Aurela, M., Bernhofer, C., Bruemmer, C., Bret-Harte, S., Chu, H., Chen, J., Desai, A.R., Dusek, J., Euskirchen, E.S., Fortuniak, K., Flanagan, L.B., Friborg, T., Grygoruk, M., Gogo, S., Gruenwald, T., Hansen, B.U., Holl, D., Humphreys, E., Hurkuck, M., Kiely, G., Klatt, J., Kutzbach, L., Largeron, C., Laggoun-Defarge, F., Lund, M., Lafleur, P.M., Li, X., Mammarella, I., Merbold, L., Nilsson, M.B., Olejnik, J., Ottosson-Lofvenius, M., Oechel, W., Parmentier, F.-J.W., Peichl, M., Pirk, N., Peltola, O., Pawlak, W., Rasse, D., Rinne, J., Shaver, G., Schmid, H.P., Sottocornola, M., Steinbrecher, R., Sachs, T., Urbaniak, M., Zona, D., Ziemblinska, K., Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), ICOS-ATC (ICOS-ATC), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), MOSAIC (MOSAIC), Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Institut des Géosciences de l’Environnement [2017-2019] (IGE [2017-2019]), Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), Department of Ecology, College of Urban and Environmental Sciences, Peking University [Beijing], Climate and Global Change Research [Helsinki], Finnish Meteorological Institute (FMI), Institute of Hydrology and Meteorology [Dresden], Technische Universität Dresden (TUD), Thünen Institute of Climate-Smart Agriculture, Institute of Arctic Biology, University of Alaska [Anchorage], Department of Environmental Science, Policy, and Management [Berkeley] (ESPM), University of California [Berkeley], University of California-University of California, Center for Global Change and Earth Observations, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Department of Atmospheric and Oceanic Sciences [Madison], University of Wisconsin-Madison, Department of Matters and Energy Fluxes, Czech Academy of Sciences [Prague] (ASCR), Department of Meteorology and Climatology [Łódz ́], University of Lódź, University of Lethbridge, Department of Geosciences and Natural Resource Management [Copenhagen] (IGN), Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Warsaw University of Life Sciences (SGGW), Institut des Sciences de la Terre d'Orléans - UMR7327 (ISTO), Bureau de Recherches Géologiques et Minières (BRGM) (BRGM)-Observatoire des Sciences de l'Univers en région Centre (OSUC), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, PSL Research University (PSL)-PSL Research University (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, PSL Research University (PSL)-PSL Research University (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS), Biogéosystèmes Continentaux - UMR7327, PSL Research University (PSL)-PSL Research University (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Bureau de Recherches Géologiques et Minières (BRGM) (BRGM)-Observatoire des Sciences de l'Univers en région Centre (OSUC), University of Hamburg, Carleton University, Department of Civil and Environmental Engineering [Cork], University College Cork (UCC), Karlsruhe Institute of Technology (KIT), Department of Bioscience and Arctic Research Center, Aarhus University [Aarhus], School of the Environment – Geography, Trent University, Department of Physics [Helsinki], Falculty of Science [Helsinki], University of Helsinki-University of Helsinki, Institute of Agricultural Sciences, Ecole Polytechnique Fédérale de Zurich, Department of Forest Ecology and Management, Swedish University of Agricultural Sciences (SLU), Department of Meteorology, Faculty of Wood Technology, Poznan' University of Life Sciences, Poznan University of Life Sciences-Poznan University of Life Sciences, Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT), German Research Centre for Geosciences - Helmholtz-Centre Potsdam (GFZ), PIVOTS project of the Région Centre – Val de Loire (ARD 2020 program and CPER 2015–2020)., ANR-10-LABX-100-01/10-LABX-0100,VOLTAIRE,Geofluids and Volatil elements – Earth, Atmosphere, Interfaces – Resources and Environment(2010), European Project: 610028,EC:FP7:ERC,ERC-2013-SyG,IMBALANCE-P(2014), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Technische Universität Dresden = Dresden University of Technology (TU Dresden), University of Alaska [Fairbanks] (UAF), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), Czech Academy of Sciences [Prague] (CAS), University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Bureau de Recherches Géologiques et Minières (BRGM) (BRGM)-Observatoire des Sciences de l'Univers en région Centre (OSUC), Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki, ANR-10-LABX-0100,VOLTAIRE,Geofluids and Volatil elements – Earth, Atmosphere, Interfaces – Resources and Environment(2010), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut de Recherche pour le Développement (IRD)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Department of Physics, and Micrometeorology and biogeochemical cycles

Subjects
1171 Geosciences, VDP::Mathematics and natural science: 400::Geosciences: 450, VDP::Landbruks- og Fiskerifag: 900, CARBON-DIOXIDE EXCHANGE, HYDRAULIC CONDUCTIVITY, Peatlands, 114 Physical sciences, Carbon, LAST GLACIAL MAXIMUM, ECOSYSTEM RESPIRATION, CHANGING CLIMATE CONDITIONS, [SDU]Sciences of the Universe [physics], LAND-SURFACE MODEL, ddc:550, NET PRIMARY PRODUCTION, TEMPERATE BOG, Earth Sciences, VDP::Matematikk og Naturvitenskap: 400::Geofag: 450, Hydrology, INTERANNUAL VARIABILITY, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, LITTER DECOMPOSITION RATES
Abstract

Peatlands store substantial amounts of carbon and are vulnerable to climate change. We present a modified version of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model for simulating the hydrology, surface energy, and CO2 fluxes of peatlands on daily to annual timescales. The model includes a separate soil tile in each 0.5° grid cell, defined from a global peatland map and identified with peat-specific soil hydraulic properties. Runoff from non-peat vegetation within a grid cell containing a fraction of peat is routed to this peat soil tile, which maintains shallow water tables. The water table position separates oxic from anoxic decomposition. The model was evaluated against eddy-covariance (EC) observations from 30 northern peatland sites, with the maximum rate of carboxylation (Vcmax) being optimized at each site. Regarding short-term day-to-day variations, the model performance was good for gross primary production (GPP) (r2 = 0.76; Nash–Sutcliffe modeling efficiency, MEF = 0.76) and ecosystem respiration (ER, r2 = 0.78, MEF = 0.75), with lesser accuracy for latent heat fluxes (LE, r2 = 0.42, MEF = 0.14) and and net ecosystem CO2 exchange (NEE, r2 = 0.38, MEF = 0.26). Seasonal variations in GPP, ER, NEE, and energy fluxes on monthly scales showed moderate to high r2 values (0.57–0.86). For spatial across-site gradients of annual mean GPP, ER, NEE, and LE, r2 values of 0.93, 0.89, 0.27, and 0.71 were achieved, respectively. Water table (WT) variation was not well predicted (r2 Vcmax and latitude (temperature), which better reflects the spatial gradients of annual NEE than using an average Vcmax value.

Published
2018
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41. ORCHIDEE-PEAT (revision 4596), a model for northern peatland CO2, water, and energy fluxes on daily to annual scales

Author

Qiu, C. (Chunjing), Zhu, D. (Dan), Ciais, P. (Philippe), Guenet, B. (Bertrand), Krinner, G. (Gerhard), Peng, S. (Shushi), Aurela, M. (Mika), Bernhofer, C. (Christian), Brümmer, C. (Christian), Bret-Harte, S. (Syndonia), Chu, H. (Housen), Chen, J. (Jiquan), Desai, A.R. (Ankur R.), Dušek, J. (Jǐrí), Euskirchen, E.S. (Eugénie S.), Fortuniak, K. (Krzysztof), Flanagan, L.B. (Lawrence B.), Friborg, T. (Thomas), Grygoruk, M. (Mateusz), Gogo, S. (Sébastien), Grünwald, T. (Thomas), Hansen, B.U. (Birger U.), Holl, D. (David), Humphreys, E. (Elyn), Hurkuck, M. (Miriam), Kiely, G. (Gerard), Klatt, J. (Janina), Kutzbach, L. (Lars), Largeron, C. (Chloé), Laggoun-Défarge, F. (Fatima), Lund, M. (Magnus), Lafleur, P.M. (Peter M.), Li, X. (Xuefei), Mammarella, I. (Ivan), Merbold, L. (Lutz), Nilsson, M.B. (Mats B.), Olejnik, J. (Janusz), Ottosson-Löfvenius, M. (Mikaell), Oechel, W. (Walter), Parmentier, F.-J.W. (Frans-Jan W.), Peichl, M. (Matthias), Pirk, N. (Norbert), Peltola, O. (Olli), Pawlak, W. (Włodzimierz), Rasse, D. (Daniel), Rinne, J. (Janne), Shaver, G. (Gaius), Peter Schmid, H. (Hans), Sottocornola, M. (Matteo), Steinbrecher, R. (Rainer), Sachs, T. (Torsten), Urbaniak, M. (Marek), Zona, D. (Donatella), Ziemblinska, K. (Klaudia), Qiu, C. (Chunjing), Zhu, D. (Dan), Ciais, P. (Philippe), Guenet, B. (Bertrand), Krinner, G. (Gerhard), Peng, S. (Shushi), Aurela, M. (Mika), Bernhofer, C. (Christian), Brümmer, C. (Christian), Bret-Harte, S. (Syndonia), Chu, H. (Housen), Chen, J. (Jiquan), Desai, A.R. (Ankur R.), Dušek, J. (Jǐrí), Euskirchen, E.S. (Eugénie S.), Fortuniak, K. (Krzysztof), Flanagan, L.B. (Lawrence B.), Friborg, T. (Thomas), Grygoruk, M. (Mateusz), Gogo, S. (Sébastien), Grünwald, T. (Thomas), Hansen, B.U. (Birger U.), Holl, D. (David), Humphreys, E. (Elyn), Hurkuck, M. (Miriam), Kiely, G. (Gerard), Klatt, J. (Janina), Kutzbach, L. (Lars), Largeron, C. (Chloé), Laggoun-Défarge, F. (Fatima), Lund, M. (Magnus), Lafleur, P.M. (Peter M.), Li, X. (Xuefei), Mammarella, I. (Ivan), Merbold, L. (Lutz), Nilsson, M.B. (Mats B.), Olejnik, J. (Janusz), Ottosson-Löfvenius, M. (Mikaell), Oechel, W. (Walter), Parmentier, F.-J.W. (Frans-Jan W.), Peichl, M. (Matthias), Pirk, N. (Norbert), Peltola, O. (Olli), Pawlak, W. (Włodzimierz), Rasse, D. (Daniel), Rinne, J. (Janne), Shaver, G. (Gaius), Peter Schmid, H. (Hans), Sottocornola, M. (Matteo), Steinbrecher, R. (Rainer), Sachs, T. (Torsten), Urbaniak, M. (Marek), Zona, D. (Donatella), and Ziemblinska, K. (Klaudia)

Abstract

Peatlands store substantial amounts of carbon and are vulnerable to climate change. We present a modified version of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model for simulating the hydrology, surface energy, and CO2 fluxes of peatlands on daily to annual timescales. The model includes a separate soil tile in each 0.5° grid cell, defined from a g

Published
2018
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42. Chapter 3: Impacts of 1.5ºC global warming on natural and human systems

Author

Hoegh - Guldberg, O., Jacob, D., Taylor, M., Bindi, M., Brown, S., Camilloni, I., Diedhiou, A., Djalante, R., Ebi, K., Engelbrecht, F., Guiot, K., Hijioka, Y., Mehrotra, S., Payne, A., Seneviratne, S., Thomas, A., Warren, R., Zhou, G., Halim, S., Achlatis, M., Alexander, L., Allen, M., Berry, P., Boyer, C., Brilli, L., Buckeridge, M., Cheung, W., Craig, M., Ellis, N., Evans, J., Fisher, H., Fraedrich, K., Fuss, S., Ganase, A., Gattuso, J., Greve, P., Guillen, T., Hanasaki, N., Hasegawa, T., Hayes, K., Hirsch, A., Jones, C., Jung, T., Kanninen, M., Krinner, G., Lawrence, D., Lenton, T., Ley, D., Liveman, D., Mahowald, N., McInnes, K., Meissner, K., Millar, R., Mintenbeck, K., Mitchell, D., Mix, A., Notz, D., Nurse, L., Okem, A., Olsson, L., Oppenheimer, M., Paz, S., Peterson, J., Petzold, J., Preuschmann, S., Rahman, M., Rogelj, J., Scheuffele, H., Schleussner, C.-F., Scott, D., Seferian, R., Sillmann, J., Singh, C., Slade, R., Stephenson, K., Stephenson, T., Sylla, M., Tebboth, M., Tschakert, P., Vautard, R., Wartenburger, R., Wehner, M., Weyer, N., Whyte, F., Yohe, G., Zhang, X., Zougmore, R., Hoegh - Guldberg, O., Jacob, D., Taylor, M., Bindi, M., Brown, S., Camilloni, I., Diedhiou, A., Djalante, R., Ebi, K., Engelbrecht, F., Guiot, K., Hijioka, Y., Mehrotra, S., Payne, A., Seneviratne, S., Thomas, A., Warren, R., Zhou, G., Halim, S., Achlatis, M., Alexander, L., Allen, M., Berry, P., Boyer, C., Brilli, L., Buckeridge, M., Cheung, W., Craig, M., Ellis, N., Evans, J., Fisher, H., Fraedrich, K., Fuss, S., Ganase, A., Gattuso, J., Greve, P., Guillen, T., Hanasaki, N., Hasegawa, T., Hayes, K., Hirsch, A., Jones, C., Jung, T., Kanninen, M., Krinner, G., Lawrence, D., Lenton, T., Ley, D., Liveman, D., Mahowald, N., McInnes, K., Meissner, K., Millar, R., Mintenbeck, K., Mitchell, D., Mix, A., Notz, D., Nurse, L., Okem, A., Olsson, L., Oppenheimer, M., Paz, S., Peterson, J., Petzold, J., Preuschmann, S., Rahman, M., Rogelj, J., Scheuffele, H., Schleussner, C.-F., Scott, D., Seferian, R., Sillmann, J., Singh, C., Slade, R., Stephenson, K., Stephenson, T., Sylla, M., Tebboth, M., Tschakert, P., Vautard, R., Wartenburger, R., Wehner, M., Weyer, N., Whyte, F., Yohe, G., Zhang, X., and Zougmore, R.

Published
2018

43. A 40-year accumulation dataset for Adelie Land, Antarctica and its application for model validation

Author

Agosta, C., Favier, V., Genthon, C., Gallée, H., Krinner, G., Lenaerts, J.T.M., van den Broeke, M.R., Marine and Atmospheric Research, Dep Natuurkunde, Sub Dynamics Meteorology, Marine and Atmospheric Research, Dep Natuurkunde, and Sub Dynamics Meteorology

Subjects
Earth sciences & physical geography [Physical, chemical, mathematical & earth Sciences], Atmospheric Science, model evaluation, 010504 meteorology & atmospheric sciences, biology, surface mass balance, obsevation, Magnitude (mathematics), 010502 geochemistry & geophysics, biology.organism_classification, 01 natural sciences, Standard deviation, Sciences de la terre & géographie physique [Physique, chimie, mathématiques & sciences de la terre], Model validation, modelling, Glacier mass balance, 13. Climate action, Climatology, Environmental science, Common spatial pattern, Antarctica, Spatial variability, Tern, 0105 earth and related environmental sciences, Chronology
Abstract

The GLACIOCLIM-SAMBA (GS) Antarctic accumulation monitoring network, which extends from the coast of Adelie Land to the Antarctic plateau, has been surveyed annually since 2004. The network includes a 156-km stake-line from the coast inland, along which accumulation shows high spatial and interannual variability with a mean value of 362 mm water equivalent a -1. In this paper, this accumulation is compared with older accumulation reports from between 1971 and 1991. The mean and annual standard deviation and the km-scale spatial pattern of accumulation were seen to be very similar in the older and more recent data. The data did not reveal any significant accumulation trend over the last 40 years. The ECMWF analysis-based forecasts (ERA-40 and ERA-Interim), a stretched-grid global general circulation model (LMDZ4) and three regional circulation models (PMM5, MAR and RACMO2), all with high resolution over Antarctica (27-125 km), were tested against the GS reports. They qualitatively reproduced the meso-scale spatial pattern of the annual-mean accumulation except MAR. MAR significantly underestimated mean accumulation, while LMDZ4 and RACMO2 overestimated it. ERA-40 and the regional models that use ERA-40 as lateral boundary condition qualitatively reproduced the chronology of interannual variability but underestimated the magnitude of interannual variations. Two widely used climatologies for Antarctic accumulation agreed well with the mean GS data. The model-based climatology was also able to reproduce the observed spatial pattern. These data thus provide new stringent constraints on models and other large-scale evaluations of the Antarctic accumulation.

Published
2012
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45. Variability in the sensitivity among model simulations of permafrost and carbon dynamics in the permafrost region between 1960 and 2009

Author

McGuire, A.D., Koven, C., Lawrence, D.M., Clein, J.S., Xia, J., BEER, C., Burke, E., Chen, G., Chen, X., Delire, C., Jafarov, E., MacDougall, A., Marchenko, S., Nicolsky, D., Peng, Shuang, Rinke, A., Saito, K., Zhang, W., Alkama, R., Bohn, T.J., Ciais, Philippe, Decharme, B., Hayes, D.J., Ekici, A., Gouttevin, I., Hajima, T., Ji, D., Krinner, G., Lettenmaier, D.P., Luo, Y., Miller, P.A., Moore, J.C., Romanovsky, V., Schaedel, C., Schaefer, K., Schuur, E.A.G., Smith, Barry, Sueyoshi, T., Zhuang, Q, Geophysical Institute [Fairbanks], University of Alaska [Fairbanks] (UAF), LAWRENCE BERKELEY NATIONAL LABORATORY CALIFORNIA USA, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), NATIONAL CENTER FOR ATMOSPHERIC RESEARCH BOULDER COLORADO USA, INSTITUTE OF ARCTIC BIOLOGY UNIVERSITY OF ALASKA FAIRBANKS USA, EAST CHINA NORMAL UNIVERSITY SHANGHAI CHN, ACES STOCKHOLM SWE, BOLIN CENTRE FOR CLIMATE RESEARCH STOCKHOLM UNIVERSITY SWE, MET OFFICE HADLEY CENTRE EXETER GBR, OAK RIDGE NATIONAL LABORATORY TENNESSEE USA, UNIVERSITY OF WAHINGTON SEATTLE USA, Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), INSTITUTE OF ARCTIC ALPINE RESEARCH UNIVERSITY OF COLORADO BOULDER USA, SCHOOL OF EARTH AND OCEAN SCIENCES UNIVERSITY OF VICTORIA BRITISH COLUMBIA CAN, Laboratoire de glaciologie et géophysique de l'environnement (LGGE), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), ALFRED WEGENER INSTITUTE HELMHOLTZ CENTRE FOR POLAR AND MARINE RESEARCH POTSDAM DEU, COLLEGE OF GLOBAL CHANGE AND EARTH SYSTEM SCIENCE BEIJING NORMAL UNIVERSITY CHN CHN, JAPAN AGENCY FOR MARINE EARTH SCIENCE AND TECHNOLOGY YOKOHAMA JPN, DEPARTMENT OF PHYSICAL GEOGRAPHY AND ECOSYSTEM SCIENCE LUND UNIVERSITY SWE, SCHOOL OF EARTH AND SPACE EXPLORATION ARIZONA STATE UNIVERSITY TEMPE USA, ICOS-ATC (ICOS-ATC), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Hydrologie-Hydraulique (UR HHLY), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), DEPARTMENT OF GEOGRAPHY UNIVERSITY OF CALIFORNIA LOS ANGELES USA, DEPARTMENT OF MICROBIOLOGY AND PLANT BIOLOGY UNIVERSITY OF OKLAHOMA NORMAN USA, NORTHERN ARIZONA UNIVERSITY FLAGSTAFF USA, NATIONAL SNOW AND ICE DATA CENTER UNIVERSITY OF COLORADO BOULDER USA, and PURDUE UNIVERSITY WEST LAFAYETTE INDIANA USA

Subjects
PERMAFROST, PERMAFROST MODELLING, MODEL SENSITIVITY, LAND-SURFACE MODELS, CO2-ENHANCEMENT, CLIMATE CHANGE, [SDE]Environmental Sciences, GROSS PRIMARY PRODUCTION, CARBON DYNAMICS
Abstract

International audience; A significant portion of the large amount of carbon (C) currently stored in soils of the permafrost region in the Northern Hemisphere has the potential to be emitted as the greenhouse gases CO2 and CH4 under a warmer climate. In this study we evaluated the variability in the sensitivity of permafrost and C in recent decades among land surface model simulations over the permafrost region between 1960 and 2009. The 15 model simulations all predict a loss of near-surface permafrost (within 3 m) area over the region, but there are large differences in the magnitude of the simulated rates of loss among the models (0.2 to 58.8 × 103 km2 yr−1). Sensitivity simulations indicated that changes in air temperature largely explained changes in permafrost area, although interactions among changes in other environmental variables also played a role. All of the models indicate that both vegetation and soil C storage together have increased by 156 to 954 Tg C yr−1 between 1960 and 2009 over the permafrost region even though model analyses indicate that warming alone would decrease soil C storage. Increases in gross primary production (GPP) largely explain the simulated increases in vegetation and soil C. The sensitivity of GPP to increases in atmospheric CO2 was the dominant cause of increases in GPP across the models, but comparison of simulated GPP trends across the 1982–2009 period with that of a global GPP data set indicates that all of the models overestimate the trend in GPP. Disturbance also appears to be an important factor affecting C storage, as models that consider disturbance had lower increases in C storage than models that did not consider disturbance. To improve the modeling of C in the permafrost region, there is the need for the modeling community to standardize structural representation of permafrost and carbon dynamics among models that are used to evaluate the permafrost C feedback and for the modeling and observational communities to jointly develop data sets and methodologies to more effectively benchmark models.

Published
2016
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46. Evaluation of air-soil temperature relationships simulated by land surface models during winter across the permafrost region

Author

Wang, W., Rinke, A., Moore, J.C., Ji, D., Cui, X., Peng, S., Lawrence, D.M., McGuire, A.D., Burke, E.J., Chen, X., Decharme, B., Koven, C., MacDougall, C., Saito, K., Zhang, W., Alkama, R., Bohn, T.J., Ciais, P., Delire, C., Gouttevin, I., Hajima, T., Krinner, G., Lettenmaier, D.P., Miller, P.A., Smith, B., Sueyoshi, T., Sherstiukov, A.B., Beijing Normal University (BNU), Laboratoire de glaciologie et géophysique de l'environnement (LGGE), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), National Center for Atmospheric Research [Boulder] (NCAR), University of Alaska [Fairbanks] (UAF), Met Office Hadley Centre for Climate Change (MOHC), United Kingdom Met Office [Exeter], University of Washington [Seattle], Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Lawrence Berkeley National Laboratory [Berkeley] (LBNL), School of Earth Sciences [Melbourne], Faculty of Science [Melbourne], University of Melbourne-University of Melbourne, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Lund University [Lund], Arizona State University [Tempe] (ASU), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), ICOS-ATC (ICOS-ATC), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Hydrologie-Hydraulique (UR HHLY), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), National Institute of Polar Research [Tokyo] (NiPR), World Data Centre, All-Russian Research Institute of Hydrometeorological Information, Beijing Normal University, Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS), Met Office Hadley Centre (MOHC), and Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA) - Grenoble

Subjects
modelling, [SDE.IE]Environmental Sciences/Environmental Engineering, temperature, [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology, snow, MODELISATION, NEIGE
Abstract

International audience; A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyse simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models, and compare them with observations from 268 Russian stations. There are large cross-model differences in the simulated differences between near-surface soil and air temperatures (ΔT; 3 to 14 °C), in the sensitivity of soil-to-air temperature (0.13 to 0.96 °C °C−1), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, hence guide improvements to the model's conceptual structure and process parameterisations. Models with better performance apply multilayer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (13.19 to 15.77 million km2). However, there is not a simple relationship between the sophistication of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, because several other factors, such as soil depth used in the models, the treatment of soil organic matter content, hydrology and vegetation cover, also affect the simulated permafrost distribution.

Published
2016
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47. Century-scale simulations of the response of the West Antarctic Ice Sheet to a warming climate

Author

Cornford, S. L., Martin, D. F., Payne, A. J., Ng, E. G., Le Brocq, A. M., Gladstone, R. M., Edwards, T. L., Shannon, S. R., Agosta, C., van den Broeke, M. R., Hellmer, H. H., Krinner, G., Ligtenberg, S. R. M., Timmermann, R., Vaughan, D. G., Sub Dynamics Meteorology, Marine and Atmospheric Research, University of Bristol [Bristol], Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Centre for Ecology and Conservation, College of Life and Environmental Sciences, University of Exeter, Penryn, UK., Université de Liège, Laboratoire de glaciologie et géophysique de l'environnement (LGGE), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Institute for Marine and Atmospheric Research [Utrecht] (IMAU), Utrecht University [Utrecht], Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), British Antarctic Survey (BAS), Natural Environment Research Council (NERC), School of Geographical Sciences, University of Bristol, Bristol, United Kingdom, Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS), Lawrence Berkeley National Laboratory [Berkeley] ( LBNL ), University of Liege Belgium, Laboratoire de glaciologie et géophysique de l'environnement ( LGGE ), Observatoire des Sciences de l'Univers de Grenoble ( OSUG ), Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Centre National de la Recherche Scientifique ( CNRS ), Institute for Marine and Atmospheric Research Utrecht ( IMAU ), Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung ( AWI ), Institute for Marine and Atmospheric Research Utrecht, British Antarctic Survey ( BAS ), Natural Environment Research Council ( NERC ), Sub Dynamics Meteorology, and Marine and Atmospheric Research

Subjects
SHELF, 010504 meteorology & atmospheric sciences, SURFACE MASS-BALANCE, Ice stream, FLOW, MODELS, Antarctic ice sheet, Antarctic sea ice, 010502 geochemistry & geophysics, 01 natural sciences, Ice shelf, [ SDE ] Environmental Sciences, Sea ice, Cryosphere, Ice divide, BED, lcsh:Environmental sciences, 0105 earth and related environmental sciences, Earth-Surface Processes, Water Science and Technology, lcsh:GE1-350, geography, geography.geographical_feature_category, lcsh:QE1-996.5, WEDDELL SEA SECTOR, RETREAT, lcsh:Geology, GROUNDING-LINE MIGRATION, 13. Climate action, Climatology, [SDE]Environmental Sciences, PINE ISLAND GLACIER, Ice sheet, SENSITIVITY, Geology
Abstract

We use the BISICLES adaptive mesh ice sheet model to carry out one, two, and three century simulations of the fast-flowing ice streams of the West Antarctic Ice Sheet. Each of the simulations begins with a geometry and velocity close to present day observations, and evolves according to variation in meteoric ice accumulation, ice shelf melting, and mesh resolution. Future changes in accumulation and melt rates range from no change, through anomalies computed by atmosphere and ocean models driven by the E1 and A1B emissions scenarios, to spatially uniform melt rates anomalies that remove most of the ice shelves over a few centuries. We find that variation in the resulting ice dynamics is dominated by the choice of initial conditions, ice shelf melt rate and mesh resolution, although ice accumulation affects the net change in volume above flotation to a similar degree. Given sufficient melt rates, we compute grounding line retreat over hundreds of kilometers in every major ice stream, but the ocean models do not predict such melt rates outside of the Amundsen Sea Embayment until after 2100. Sensitivity to mesh resolution is spurious, and we find that sub-kilometer resolution is needed along most regions of the grounding line to avoid systematic under-estimates of the retreat rate, although resolution requirements are more stringent in some regions – for example the Amundsen Sea Embayment – than others – such as the Möller and Institute ice streams.

Published
2015
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48. Simulating the thermal regime and thaw processes of ice-rich permafrost ground with the land-surface model CryoGrid 3

Author

Westermann, S., Langer, Moritz, Boike, J., Heikenfeld, M., Peter, M., Etzelmüller, B., Krinner, G., Westermann, S., Langer, Moritz, Boike, J., Heikenfeld, M., Peter, M., Etzelmüller, B., and Krinner, G.

Abstract

Thawing of permafrost in a warming climate is governed by a complex interplay of different processes of which only conductive heat transfer is taken into account in most model studies. However, observations in many permafrost landscapes demonstrate that lateral and vertical movement of water can have a pronounced influence on the thaw trajectories, creating distinct landforms, such as thermokarst ponds and lakes, even in areas where permafrost is otherwise thermally stable. Novel process parameterizations are required to include such phenomena in future projections of permafrost thaw and subsequent climatic-triggered feedbacks. In this study, we present a new land-surface scheme designed for permafrost applications, CryoGrid 3, which constitutes a flexible platform to explore new parameterizations for a range of permafrost processes. We document the model physics and employed parameterizations for the basis module CryoGrid 3, and compare model results with in situ observations of surface energy balance, surface temperatures, and ground thermal regime from the Samoylov permafrost observatory in NE Siberia. The comparison suggests that CryoGrid 3 can not only model the evolution of the ground thermal regime in the last decade, but also consistently reproduce the chain of energy transfer processes from the atmosphere to the ground. In addition, we demonstrate a simple 1-D parameterization for thaw processes in permafrost areas rich in ground ice, which can phenomenologically reproduce both formation of thermokarst ponds and subsidence of the ground following thawing of ice-rich subsurface layers. Long-term simulation from 1901 to 2100 driven by reanalysis data and climate model output demonstrate that the hydrological regime can both accelerate and delay permafrost thawing. If meltwater from thawed ice-rich layers can drain, the ground subsides, as well as the formation of a talik, are delayed. If the meltwater pools at the surface, a pond is formed that enhances heat

Published
2016

49. Permafrost landscapes in transition - towards modeling interactions, thresholds and feedbacks related to ice-rich ground

Author

Westermann, S., Langer, Moritz, Lee, H., Berntsen, T., Boike, Julia, Krinner, G., Aalstad, K., Schanke Aas, K., Peter, M., Heikenfeld, M., Etzelmüller, B., Westermann, S., Langer, Moritz, Lee, H., Berntsen, T., Boike, Julia, Krinner, G., Aalstad, K., Schanke Aas, K., Peter, M., Heikenfeld, M., and Etzelmüller, B.

Abstract

Thawing of permafrost is governed by a complex interplay of different processes, of which only conductive heat transfer is taken into account in most model studies. However, heat conduction alone can not account for the dynamical evolution of many permafrost landscapes, e.g. in areas rich in ground ice shaped by thermokarst ponds and lakes. Novel process parameterizations are required to include such phenomena in future projections of permafrost thaw and hereby triggered climatic feedbacks. Recently, we have demonstrated a physically-based parameterization for thaw process in ice-rich ground in the per-mafrost model CryoGrid 3, which can reproduce the formation of thermokarst ponds and subsidence of the ground following thawing of ice-rich subsurface layers. Long-term simulations for different subsurface stratigraphies in the Lena River Delta, Siberia, demonstrate that the hydrological regime can both accelerate and delay permafrost thawing. If meltwater from thawed ice-rich layers can drain, the ground subsides while at the same time the for-mation of a talik is delayed. If the meltwater pools at the surface, a pond is formed which enhances heat transfer in the ground and leads to the formation of a talik. The PERMANOR project funded by the Norwegian Research Council until 2019 will extend this work by inte- grating such small-scale processes in larger-scale Earth System Models (ESMs). For this purpose, the project will explore and develop statistical approaches, in particular tiling, to represent permafrost landscape dynamics on sub-grid scale. Ultimately, PERMANOR will conceptualize process understanding from in-situ studies to develop new model algorithms and pursue their implementation in a coupled ESM framework

Published
2016

50. Carbon stocks and fluxes in the high-latitudes: Using site-level data to evaluate earth system models

Author

Chadburn, S., Burke, E., Ekici, A., Beer, C., Porada, P., Krinner, G., Wang, T., Brutel-Vuilmet, C., Belelli Marchesini, L., van Huissteden, K., Kutzbach, L., Hugelius, G., Boike, Julia, Langer, Moritz, Elberling, B., Johansson, M., Westermann, S., Chadburn, S., Burke, E., Ekici, A., Beer, C., Porada, P., Krinner, G., Wang, T., Brutel-Vuilmet, C., Belelli Marchesini, L., van Huissteden, K., Kutzbach, L., Hugelius, G., Boike, Julia, Langer, Moritz, Elberling, B., Johansson, M., and Westermann, S.

Abstract

The high latitudes form an important component of the Earth’s carbon cycle. It is therefore important to capture this in Earth System Models (ESM’s). However, most carbon-cycle development and evaluation in ESM’s focuses on lower latitudes, and therefore there is an urgent need to address Arctic carbon-cycle processes. Here, we run land-surface schemes from ESM’s at the site level at various Arctic sites, performing a detailed evaluation of the carbon dynamics in the models. They are process-based models, and therefore point-scale evaluations contribute directly towards improving the large-scale results. The sites chosen for the simulations are the five principal field sites from the recently-concluded EU project PAGE21. This gives the distinct advantage that detailed data are available. In particular, data on the physical state of the climate and permafrost at these sites, and large datasets of soil carbon stocks and fluxes. The sites cover a range from low Arctic discontinuous permafrost to high Arctic desert, and a range of soil types from thick peat to mineral soils with little organic matter. The models involved are land surface schemes from three European ESM’s: UKESM (UK), IPSL (France) and MPI-ESM (Germany). The models all have improved process representation as part of PAGE21. The simulations are first compared with physical observations from the sites: specifically snow depth, soil temperature, soil moisture and maximum thaw depth. All models capture the physics with a reasonable accuracy, and certainly capture the major differences between sites, with a few exceptions. In particular, we see the importance of simulating the physical properties of the soil organic layer. Comparing simulated soil organic carbon with observations shows the importance of including vertical soil carbon profiles. In one model this is not represented, which results in a failure to capture the differences in soil carbon in different physical conditions. Including cryoturbation mix

Published
2016

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