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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.

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.

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.

Irrigation water use associated with agricultural activities and urban green spaces provides substantial cooling effects and ameliorates heat in central Arizona. In this arid and semiarid area, evaluating the effect of irrigation on land surface temperature (LST) for different types of land use can improve decision making related to water resources management. In this work, we improved the simulation of urban and agricultural irrigation in the Variable Infiltration Capacity model through remotely sensed vegetation and irrigation parameters applied at high spatiotemporal resolution. We then conducted a multiyear (2004-2013) assessment of simulated LST with respect to ground observations and remotely sensed products finding overall good agreement. Overall, results show that irrigation of about 2 mm/day is required to reduce average daily LST by 1°C across the region. Numerical experiments with the validated model also reveal that irrigation leads to LST reductions of higher magnitude and greater spatial variability in croplands than in urban areas. Furthermore, we found that the role of interannual variations in cropping practices is more critical than year-to-year differences in climatic conditions for the evaluation of irrigation cooling capacity. Thus, remotely sensed vegetation products can serve a valuable purpose in quantifying LST reductions and irrigation requirements to achieve a target of heat amelioration. [ABSTRACT FROM AUTHOR]

Developing interdisciplinary methods to measure ecosystem services is a scientific priority, however, progress remains slow in part because we lack ecological production functions (EPFs) to quantitatively link ecohydrological processes to human benefits. In this study, we tested a new approach, combining a process-based model with regression models, to create EPFs to evaluate water storage and local climate regulation from a green infrastructure project on the Yongding River in Beijing, China. Seven artificial lakes and wetlands were established to improve local water storage and human comfort; evapotranspiration (ET) regulates both services. Managers want to minimize the trade-off between water losses and cooling to sustain water supplies while lowering the heat index (HI) to improve human comfort. We selected human benefit indicators using water storage targets and Beijing's HI, and the Variable Infiltration Capacity model to determine the change in ET from the new ecosystems. We created EPFs to quantify the ecosystem services as marginal values [Δfinal ecosystem service/Δecohydrological process]: (1) Δwater loss (lake evaporation/volume)/Δdepth and (2) Δsummer HI/ΔET. We estimate the new ecosystems increased local ET by 0.7 mm/d (20.3 W/m2) on the Yongding River. However, ET rates are causing water storage shortfalls while producing no improvements in human comfort. The shallow lakes/wetlands are vulnerable to drying when inflow rates fluctuate, low depths lead to higher evaporative losses, causing water storage shortfalls with minimal cooling effects. We recommend managers make the lakes deeper to increase water storage, and plant shade trees to improve human comfort in the parks. [ABSTRACT FROM AUTHOR]

The sensitivity of Earth's wetlands to observed shifts in global precipitation and temperature patterns and their ability to produce large quantities of methane gas are key global change questions. We present a microwave satellite-based approach for mapping fractional surface water (FW) globally at 25-km resolution. The approach employs a land cover-supported, atmospherically-corrected dynamic mixture model applied to 20+ years (1992-2013) of combined, daily, passive/active microwave remote sensing data. The resulting product, known as Surface WAter Microwave Product Series (SWAMPS), shows strong microwave sensitivity to sub-grid scale open water and inundated wetlands comprising open plant canopies. SWAMPS' FW compares favorably (R²2 = 91%-94%) with higher-resolution, global-scale maps of open water from MODIS and SRTM-MOD44W. Correspondence of SWAMPS with open water and wetland products from satellite SAR in Alaska and the Amazon deteriorates when exposed wetlands or inundated forests captured by the SAR products were added to the open water fraction reflecting SWAMPS' inability to detect water underneath the soil surface or beneath closed forest canopies. Except for a brief period of drying during the first 4 years of observation, the inundation extent for the global domain excluding the coast was largely stable. Regionally, inundation in North America is advancing while inundation is on the retreat in Tropical Africa and North Eurasia. SWAMPS provides a consistent and long-term global record of daily FW dynamics, with documented accuracies suitable for hydrologic assessment and global change-related investigations. [ABSTRACT FROM AUTHOR]