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This is the second of a two-part article investigating the impact of variations of land surface evaporability on the interannual variability of precipitation. The first goal of this part is to analyze the relationship between the atmospheric internal variability and the evaporative forcings. The hypothesis that the sum of ocean- and atmosphere-induced variabilities can be linearly amplified by the land variability is critically revisited and generally found not applicable to the climate model used and the numerical experiments conducted. A set of parameters to evaluate the departure from the linear behavior is defined, quantifying the impact of the different forcings over the total variability. Some areas of the world (e.g., the monsoon region, the continental United States, and southeastern Africa), where the impact of internal atmospheric dynamics on precipitation variability is small compared to the impact of the evaporative forcings, are localized. Over these areas, the variability of precipi...

Current techniques to disentangle the total evaporative flux from the continental surface into a contribution evaporated from soils and canopy, or transpired by plants are under debate. Many isotope-based studies show that transpiration contributes generally more than 70% to the total moisture fluxes, while other isotope-independent techniques lead to considerably smaller transpiration fractions. This paper provides a perspective on isotope-based vs. non isotope-based partitioning studies. Some partitioning results from isotope-based methods, hydrometric measurements, and modeling are presented for comparison. Moreover, the methodological aspects of the analysis of partitioning are discussed including their limitations, and explanations of possible discrepancies between the methods are briefly discussed. We conclude that every method has its own uncertainties and these may lead to a high bias in the results, e.g. instruments inaccuracy and error, some assumptions used in analyses, parameters calibration. A number of comparison studies using isotope-based methods and hydrometric measurements in the same plants and climatic conditions are consistent within the errors, however, models tend to produce lower transpiration fractions. The relatively low transpiration fractions in current state of the art land surface models calls for a reassessment of the skill of the underlying model parameterizations. The scarcity of global evaporation data makes calibration and validation of global isotope-independent and isotope-based results difficult. However, isotope enabled land-surface and global climate modeling studies allow the evaluation of the parameterization of land surface models by comparing the computed water isotopologue signals in the atmosphere with the available remote sensing and flux-based data sets. Future studies that allow this evaluation could provide a better understanding of the hydrological cycle in vegetated regions.

Current techniques to disentangle the evaporative fluxes from the continental surface into a contribution evaporated from soils and canopy, or transpired by plants, are under debate. Many isotope-based studies show that transpiration contributes generally more than 70% to the total evaporation, while other isotope-independent techniques lead to considerably smaller transpiration fractions. This paper provides a perspective on isotope-based versus non-isotope-based partitioning studies. Some partitioning results from isotope-based methods, hydrometric measurements, and modeling are presented for comparison. Moreover, the methodological aspects of the partitioning analysis are considered, including their limitations, and explanations of possible discrepancies between the methods are discussed. We suggest sources of systematic error that may lead to biases in the results, e.g., instruments inaccuracy, assumptions used in analyses, and calibration parameters. A number of comparison studies using isotope-based methods and hydrometric measurements in the same plants and climatic conditions are consistent within the errors; however, models tend to produce lower transpiration fractions. The relatively low transpiration fraction in current state-of-the-art land-surface models calls for a reassessment of the skill of the underlying model parameterizations. The scarcity of global evaporation data makes calibration and validation of global isotope-independent and isotope-based results difficult. However, isotope-enabled land-surface and global climate modeling studies allow for the evaluation of the parameterization of land-surface models by comparing the computed water isotopologue signals in the atmosphere with the available remote sensing and flux-based data sets. Future studies that allow for this evaluation could provide a better understanding of the hydrological cycle in vegetated regions.

The terrestrial and oceanic sources of moisture that supply warm-season rainfall to the Mississippi River basin and its subbasins are examined over a 36-yr period (1963–98). Using hourly observed precipitation, National Centers for Environmental Prediction (NCEP) reanalyses at 6-h intervals, and a back-trajectory algorithm, the water falling during observed precipitation events is probabilistically traced to its most recent surface evaporative source, terrestrial or oceanic. Maps of these sources generally show dual maxima, one terrestrial and one oceanic, in spring and a dominance of terrestrial sources in summer. Pentad time series averaged over the 36 years show a late-summer maximum of precipitation recycling in all but the Missouri subbasin. During the 36 years analyzed, 32% of warm-season precipitation in the entire Mississippi basin originated as evaporation within the basin (recycled). About 20% of warm-season precipitation was contributed directly by evaporation from the Gulf of Mexico a...

Two ensembles of 1-month integrations of a coupled land–atmosphere climate model that differ only in their treatment of land surface boundary conditions have been generated from initial conditions chosen from the July states taken from each year of a 17-yr integration from the second Atmospheric Model Intercomparison Project (AMIP2). Both ensembles have specified sea surface temperature from one randomly chosen year, but one ensemble has the land surface state variables specified in each member at each time step to be identical to those from a single member of the other ensemble. Comparisons with the 17-yr AMIP2 integration provide an estimate of the role of interannually varying SST in affecting climate variability. Comparison between the two ensembles helps to quantify the role of land surface variability on the variance of surface fluxes and the climate. In this model system, the impacts of suppressed ocean variability on intra-ensemble spread are generally stronger than for suppressed land su...

The International Workshop on Soil Moisture Monitoring, Analysis, and Prediction for Hydrometeorological and Hydroclimatological Applications considered the potential for implementing a global system during this decade and for identifying the priorities for the research needed to achieve such a global system. The workshop attendees advised that a global system should provide measurements and/or estimates of volumetric soil water content on at least a daily basis at 50–km resolution and at vertical resolutions consistent with vegetative rooting depths within the resolution grid. It was generally agreed that a global soil moisture monitoring system would be model based with in situ and remotely sensed measurements used primarily for validation and evaluation of the model–derived fields. The consensus of the workshop participants was that a global system for soil moisture is needed and, furthermore, a system could be implemented on an experimental basis with tools currently available. However, many ...

Potential predictability and skill of simulated Eurasian snow cover are explored using a suite of seasonal ensemble hindcasts (i.e. retrospective forecasts), an ensemble climate simulation (spanning the years 1982–1998) and observations. Using remotely sensed observations of snow cover, we find significant point-wise correlation over the North Atlantic and North Pacific between winter and spring averaged sea-surface temperatures and Eurasian snow cover area. The observed correlation shows no discernible pattern related to the El Nino-Southern Oscillation (ENSO). The hindcasts show correlation patterns similar to the observations. However, the climate simulation shows an exaggerated ENSO pattern. The results underscore the importance of initialization in seasonal climate forecasts, and that the observed potential predictability of Eurasian snowcover cannot be solely attributed to ENSO.

A coupled land–atmosphere climate model is examined for evidence of climate drift in the land surface state variable of soil moisture. The drift is characterized as pathological error growth in two different ways. First is the systematic error that is evident over seasonal timescales, dominated by the error modes with the largest saturated amplitude: systematic drift. Second is the fast-growing modes that are present in the first few days after either initialization or a data assimilation increment: incremental drift. When the drifts are robust across many ensemble members and from year to year, they suggest a source of drift internal to the coupled system. This source may be due to problems in either component model or in the coupling between them. Evidence is presented for both systematic and incremental drift. The relationship between the two types of drift at any given point is shown to be an indication of the type and strength of feedbacks within the coupled system. Methods for elucidating p...

This is the first of a two-part study to construct a vegetation map around the Mediterranean Sea that is an accurate representation of conditions two millennia ago, and to use this data in general circulation model experiments to better understand historical climate and climate change. Particularly, we want to evaluate the sensitivity of the atmospheric circulation around the Mediterranean Sea to changed land surface conditions. The idea that 2000 years BP, during the Roman Classical Period (RCP), northern Africa and the Mediterranean countries were moister than today is widely acknowledged but has not been discussed within a scientific framework until a few decades ago. Archeological and historical documents provide some qualitative agreement that moister conditions prevailed during Roman times, and interdisciplinary scientific research provides some confidence through proxies that the Mediterranean has been experiencing a trend towards drier conditions. The first step of this work is the multidisciplinary task of organizing all the available information coming from historical and archeological sources, together with what is known from scientific methods, into a coherent history of climate and vegetation. The second step is to run a 25-year general circulation model (GCM) experiment, with a forcing that represents an idealized RCP vegetation distribution. The forcing produces a noticeable effect on the climate, namely a northward shift of the intertropical convergence zone during summer over the African continent.

Evaporative fraction (EF; the ratio of latent heat flux to the sum of the latent plus sensible heat fluxes) can be measured in the field to an accuracy of about 10%. In this modeling study, the authors try to determine to what accuracy soil moisture must be known in order to simulate surface energy fluxes within this observational uncertainty and whether there is a firm relationship between the variabilities of soil moisture and surface turbulent energy fluxes. A relationship would provide information for planning the future measurement of soil moisture, the design of field experiments, and points of focus for soil model development. The authors look for relationships in three different land surface schemes using results and ancillary integrations in the Global Soil Wetness Project. It is found that the variation of evaporative fraction as a function of soil moisture is consistent among the models and within subsets of vegetation type. In forested areas, there is high sensitivity of EF to soil mo...

Using hourly observed precipitation data, National Centers for Environmental Prediction reanalyses at 6 hourly intervals, and a quasi-isentropic back-trajectory algorithm, we have examined the transport and surface sources of moisture supplying rainfall during the spring and summer over the United States during the drought year of 1988 and the flood year of 1993. These results are compared to calculations using a bulk-transport approach and monthly mean data. We find that about 41 % of precipitation over the Mississippi River basin originated as evaporation from the same basin during April-July 1988 and 33 % during 1993. During the July peak of the 1993 flood the recycling ratio was considerably lower than in other months, while the source of moisture from the western Gulf of Mexico and Caribbean Sea increased enormously. By contrast, at the June climax of the 1988 drought, the recycling ratio reaches a maximum. We also find a small source region in the eastern subtropical Pacific Ocean during both years, which decreases significantly with the onset of the Mexican monsoon around the beginning of July. The back-trajectory approach represents an improvement over the bulk recycling approach by rejecting the assumption of uniform distributions of rainfall in space and time, instead focusing specifically on the sources and transport of moisture contributing to observed rainfall events.

The Global Soil Wetness Project (GSWP) is an ongoing land surface modeling activity of the International Satellite Land-Surface Climatology Project (ISLSCP), a part of the Global Energy and Water Cycle Experiment. The pilot phase of GSWP deals with the production of a two-year global dataset of soil moisture, temperature, runoff, and surface fluxes by integrating uncoupled land surface schemes (LSSs) using externally specified surface forcings from observations and standardized soil and vegetation distributions. Approximately one dozen participating LSS groups in five nations have taken the common ISLSCP forcing data to drive their state-of-the-art models over the 1987–88 period to generate global datasets. Many of the LSS groups have performed specific sensitivity studies, which are intended to evaluate the impact of uncertainties in model parameters and forcing fields on simulation of the surface water and energy balances. A validation effort exists to compare the global products to other forms...

The role of land-sea geometry in determining the existence and character of monsoon circulations is examined using an atmospheric general circulation model with land surface properties represented by the simplified simple biosphere model. Idealized land-sea distributions are used to examine the effects of the latitude of continental coastlines, the meridional and zonal extent of continents, the shape of continents, and the presence of orography on circulation in the tropics and subtropics. It is found that the latitude of the continent is critical to the establishment of a distinct seasonality of precipitation and a summer monsoon circulation. Low-latitude positions of the continent lead to landlocked precipitation maxima, and poleward positions lead to a Mediterranean (winter monsoon) climate. In every case, the subsiding branch of the Hadley cell at continental longitudes locks over some part of the land. Variations in the meridional extent of land affect the strength of the Hadley cell and the degree of seasonality in the climate over land. The extension of a subtropical continent into the tropics is critical for establishing heavy convective rainfall over land in the summer. When tropical peninsulas are added to a subtropical continent, the distribution of moisture and rain over semi-arid and arid regions of the original continent are affected. When a broad mountain of 1200 m maximum elevation is present on the idealized continent, there is a band of increased precipitation to the south and east of the mountain with a systematic decrease to the south, north, and west of the band.

Fully coupled global climate model experiments are performed using the Community Climate System Model version 4.0 (CCSM4) for preindustrial, present, and future climate to study the effects of realistic land surface initializations on subseasonal to seasonal climate forecasts. Model forecasts are verified against model control simulations (perfect model experiments), thus overcoming to some extent issues of uncertainties in the observations and/or model parameterizations. Findings suggest that realistic land surface initialization is important for climate predictability at subseasonal to seasonal time scales. We found the highest predictability for soil moisture, followed by evapotranspiration, temperature, and precipitation. The predictability is highest for the 16 to 30 days forecast period, and it progressively decreases for the second and third month forecasts. We found significant changes in the spatial distributions of temperature predictability in the present and future climate compared to the preindustrial climate, although the spatial average changes for North America were rather small (

陸面数値モデル(LSP)は大気数値モデルなどに組み込まれて地表面での水とエネルギーの交換を表現している.全球土壌水分プロジェクト(GSWP)において,いつかのLSPにより1987年および1988年の水収支が,共通の降水量や放射強度に対して算定された.ここでは,全球1度グリッドメッシュで作成された河川流路網情報(TRIP)を利用し,河川流量データによってそれらLSPによる年水収支算定値を検証した.その結果,充分な降水観測(雨量計密度≧30個/l06km2)に基づいて入力降水量が算定されている領域ではLSPは適切に年流出高を算定していることがわかった.

We have proposed a new methodology to reduce uncertainties in regional climate hindcasts and projections. The fundamental premise of this work is observed regional climate trends e.g. temperature trend is a combination of two major components: (1) natural climate variability signal, and (2) global climate change signal in the region concerned. The new methodology combines regional natural (low frequency) climate variability with the global change signal from Coupled Model Inter-comparison projects phase 5 (CMIP5) climate models. While most parts of the World have shown a warming trend during the 20 th century, the eastern United States has shown a cooling trend or lack of warming trend particularly after 1930’s. Using this anomalous temperature trends’ example, we have first identified relevant natural climate variability signal. We found that the North Atlantic-Multi-decadal oscillation (AMO) explains approximately 80% variance in observed trend variability in the Eastern United States. We employed AMO as natural climate variability signal and CMIP5 median trend as global climate change signal in the study. We found the proposed methodology provides a better model than all individual CMIP5 models, and the natural climate variability only model for the 20 th century temperature trends hindcasts in the Eastern United States. For 21 st century projections, we developed a statistical model for AMO variability (65-to 70 years oscillation cycle) using past 155 years of observations. We relied on AMO statistical model because: (1) CMIP5 models have limited AMO simulation skill, and (2) it is rather difficult to extract AMO signal from CMIP5 simulations. We combined AMO simulations with the CMIP5 median trend for the 21 st century temperature trends projections in the Eastern United States. The proposed new methodology shows significant potential in reducing uncertainties in the Eastern United States temperature trends’ projections.

Land evapotranspiration (ET) estimates are available from several global data sets. Here, monthly global land ET synthesis products, merged from these individual data sets over the time periods 1989–1995 (7 yr) and 1989–2005 (17 yr), are presented. The merged synthesis products over the shorter period are based on a total of 40 distinct data sets while those over the longer period are based on a total of 14 data sets. In the individual data sets, ET is derived from satellite and/or in situ observations (diagnostic data sets) or calculated via land-surface models (LSMs) driven with observations-based forcing or output from atmospheric reanalyses. Statistics for four merged synthesis products are provided, one including all data sets and three including only data sets from one category each (diagnostic, LSMs, and reanalyses). The multi-annual variations of ET in the merged synthesis products display realistic responses. They are also consistent with previous findings of a global increase in ET between 1989 and 1997 (0.13 mm yr−2 in our merged product) followed by a significant decrease in this trend (−0.18 mm yr−2), although these trends are relatively small compared to the uncertainty of absolute ET values. The global mean ET from the merged synthesis products (based on all data sets) is 493 mm yr−1 (1.35 mm d−1) for both the 1989–1995 and 1989–2005 products, which is relatively low compared to previously published estimates. We estimate global runoff (precipitation minus ET) to 263 mm yr−1 (34 406 km3 yr−1) for a total land area of 130 922 000 km2. Precipitation, being an important driving factor and input to most simulated ET data sets, presents uncertainties between single data sets as large as those in the ET estimates. In order to reduce uncertainties in current ET products, improving the accuracy of the input variables, especially precipitation, as well as the parameterizations of ET, are crucial.

Two data assimilation (DA) methods, a simple rule-based direct insertion (DI) approach and a one-dimensional ensemble Kalman filter (EnKF) method, are evaluated by assimilating snow cover fraction observations into the Community Land surface Model. The ensemble perturbation needed for the EnKF resulted in negative snowpack biases. Therefore, a correction is made to the ensemble bias using an approach that constrains the ensemble forecasts with a single unperturbed deterministic LSM run. This is shown to improve the final snow state analyses. The EnKF method produces slightly better results in higher elevation locations, whereas results indicate that the DI method has a performance advantage in lower elevation regions. In addition, the two DA methods are evaluated in terms of their overall impacts on the other land surface state variables (e.g., soil moisture) and fluxes (e.g., latent heat flux). The EnKF method is shown to have less impact overall than the DI method and causes less distortion of the hydrological budget. However, the land surface model adjusts more slowly to the smaller EnKF increments, which leads to smaller but slightly more persistent moisture budget errors than found with the DI updates. The DI method can remove almost instantly much of the modeled snowpack, but this also allows the model system to quickly revert to hydrological balance for nonsnowpack conditions. Key Points Snow cover assimilated via direct insertion(DI) and ensemble Kalman filter(EnKF) EnKF method performed better in higher elevations and DI in lower elevations Versus DI, EnKF leads to smaller model impacts but more hydrologic budget errors ©2013. American Geophysical Union. All Rights Reserved. ispartof: Journal of Geophysical Research: Atmospheres vol:118 issue:14 pages:7489-7504 status: published

An atmospheric general-circulation model with realistic land-surface properties is used to investigate the climatic effect of doubling the extent of the earth's deserts. Control and anomaly integrations are performed for 10 years. In the anomaly case, deserts are expanded over northern and southern Africa, Australia, south-central Asia, south-western North America, and parts of South America. In the anomaly case, the troposphere is cooler across most of the tropics and subtropics, including all areas where desertification occurs. Remote effects in the winter circulation include a pronounced trough over northern Europe, and increased geopotential heights over the southern oceans. The response of climate in deforested areas is not uniform between the regions. The magnitude and seasonality of the changes, particularly in precipitation, seem to be functions of the monsoon regime. Northern Africa suffers a strong year-round drought, suggesting this area is most sensitive to desertification, and southern Africa has a somewhat weaker year-round drought. Rainfall over desertified parts of Asia, Australia and western South America decreases only during the summer, and not during the winter. North America and the Nordeste region of Brazil show the least sensitivity to desertification. Most regions show a notable correlation between decreases in evapotranspiration and resulting precipitation. However, regional land–sea distributions in the vicinity of each desertified area appear to determine the sensitivity and response of the local climate via induced changes in moisture flux convergence. Thus, different regions exhibit different responses of climate to desertification. Surface temperature decreases over most desertified areas, owing to reduced absorption of short-wave radiation by the brighter surface. Some regions, particularly the Sahel, show an increase in surface temperature caused by decreased soil moisture and latent-heat flux.

Previous estimates of land-atmosphere interaction (the impact of soil moisture on precipitation) have been limited by a lack of observational data and by the model dependence of computational estimates. To counter the second limitation, a dozen climate-modeling groups have recently performed the same highly controlled numerical experiment as part of a coordinated comparison project. This allows a multimodel estimation of the regions on Earth where precipitation is affected by soil moisture anomalies during Northern Hemisphere summer. Potential benefits of this estimation may include improved seasonal rainfall forecasts.

To assess the biogeophysical impacts of land cover/land use change (LCLUC) on surface temperature, two observation-based metrics and their applicability in climate modeling were explored in this study. Both metrics were developed based on the surface energy balance, and provided insight into the contribution of different aspects of land surface change (such as albedo, surface roughness, net radiation and surface heat fluxes) to changing climate. A revision of the first metric, the intrinsic biophysical mechanism, can be used to distinguish the direct and indirect effects of LCLUC on surface temperature. The other, a decomposed temperature metric, gives a straightforward depiction of separate contributions of all components of the surface energy balance. These two metrics well capture observed and model simulated surface temperature changes in response to LCLUC. Results from paired FLUXNET sites and land surface model sensitivity experiments indicate that surface roughness effects usually dominate the direct biogeophysical feedback of LCLUC, while other effects play a secondary role. However, coupled climate model experiments show that these direct effects can be attenuated by large scale atmospheric changes (indirect feedbacks). When applied to real-time transient LCLUC experiments, the metrics also demonstrate usefulness for assessing the performance of climate models and quantifying land–atmosphere interactions in response to LCLUC.

[1] The ability of soil moisture to affect precipitation (SM-P) can be dissected into the ability of soil moisture to affect evapotranspiration (ET; SM-ET) and the ability of ET to affect precipitation (ET-P). SM-ET is a local process that is relatively easy to quantify, but ET-P includes nonlocal atmospheric processes and is more complex. Here, ET-P is quantified both locally and remotely with a back-trajectory method for water vapor transport, using corrected reanalysis data. It is found that, for SM-P and ET-P, local impact is greater than that from remote for most land areas with significant local impacts. By examining the responses of the three metrics (SM-ET, ET-P, and SM-P) to climate variations over different climate regimes, we show that SM-ET is the principal factor that determines the spatial pattern and variation of SM-P. For climatologically wet regions, SM-ET and SM-P are higher during dry periods, and vice versa for climatologically dry regions. All three metrics show highest values over the transitional zones.

It is now well-established that the chaotic nature of the atmosphere severely limits the predictability of weather, while the slowly varying sea surface temperature (SST) and land surface states can enhance the predictability of atmospheric variations through surfaceatmosphere interactions or by providing a boundary condition (e.g., Shukla 1993, 1998; Shukla et al. 2000; Graham et al. 1994; Koster et al. 2000; Dirmeyer et al. 2003; Quan et al. 2004). Among them, the influence of ocean is more important on a global scale because it covers twice as much surface area as land and is a much larger heat and energy reservoir. But the impact of ocean may not be dominant over land, especially the mid-latitude land (Koster and Suarez 1995). The Global Land-Atmosphere Coupling Experiment (GLACE) (Koster et al., 2004, 2006) builds a framework to objectively estimate the potential contribution of land states to atmospheric predictability (called land-atmosphere coupling strength) in numerical weather and climate models. By averaging the estimated land-atmosphere coupling strength from 12 models participating in GLACE, an ensemble average coupling strength is obtained. However, the coupling strength varies widely among models. The discrepancy is certainly related to differences in the parameterization of processes and their complex interactions, from soil hydrology, vegetation physiology, to boundary layer, cloud and precipitation processes. It is difficult to determine what causes the relatively strong or weak coupling strengths seen in individual models. Some studies have identified the impact of soil moisture on evapotranspiration (ET) (denoted SM→ET) and the impact of ET on precipitation (denoted ET→P) as two key factors for land-atmosphere coupling (Guo et al. 2006 (hereafter GUO06); Dirmeyer et al. 2010). For soil moisture to have a strong impact on precipitation, both SM→ET and ET→P need to be strong. This usually happens in transitional zones between wet and dry climates (Dirmeyer 2006). In addition to the mean climate state, does the climate variability have some impact on land-atmosphere coupling? A theoretical study found that the strength of the external forcing can affect the coupling strength and the location of coupling hot spots (Wei et al. 2006). Even less is known about how the land-atmosphere coupling is related to the

An atmospheric general circulation model with land surface properties represented by the Simplified Simple Biosphere Model is used to investigate the effect of soil moisture and vegetation stress on drought in the mid-latitudes. An idealized land-sea distribution with simple topography is used to remove as many external sources of climate variation as possible. The land consists of a single, flat, rectangular continent covered with prairie vegetation and centered on 44 deg N of an aqua planet. A control integration of 4 years is performed, and several sets of seasonal anomaly integrations are made to test the sensitivity of seasonal climate to low initial (1 April) soil moisture and dormant vegetation like what would occur during a severe drought. It is found that the inclusion of dormant vegetation during the spring and early summer greatly reduces evapotranspiration by eliminating transpiration. This affects local climate more strongly as summer progresses. Low initial soil moisture, combined with dormant vegetation, leads to a severe drought. The reduction in precipitation is much greater in magnitude than that due to low soil moisture alone, and greater than the sum of the effects computed separately. Although the short-term drought is more severe, the dormancy of the vegetation prevents further depletion of moisture in the root zone of the soil, so soil moisture begins to rebound toward the middle of summer.

[1] This paper shows that realistically initialized land surface states enhance atmospheric predictability significantly out to two-to-three months during summer. The spatial structure of the impact of land initialization on atmospheric predictability can be explained by the simultaneous influence of soil moisture memory time and land surface-evapotranspiration coupling strength. A proxy for this impact based on soil moisture and evaporation anomalies is proposed. The results also show that the impact of the land surface on atmospheric predictability varies with season: enhancement of predictability is relatively small during boreal spring and autumn, and reaches a maximum during boreal summer. Remarkably, the predictability of atmospheric temperature and precipitation increases with lead time from spring to summer. This increase is diagnosed as a “transfer” of predictability from land to atmosphere: during spring, the soil moisture predictability is high, but this predictability does not impact the atmosphere due to lack of land-atmosphere coupling; during summer, the coupling increases, thereby transferring the predictability from land to atmosphere.

[1] Large scale snow-atmosphere coupling strength (the degree to which the atmosphere responds to anomalies in snow cover) is quantitatively investigated using the Community Land Model (CLM 3.5) and its advanced snow scheme coupled to the Community Atmospheric Model (CAM). An index based on model ensemble statistics has been used to quantify the coupling strength. Multi-seasonal experiments reveal the spatial and temporal distribution of coupling strength during the snow depletion phase (March through June) over the Northern Hemisphere. The snow coupling strength was found to evolve in three distinct stages: before snowmelt, during snowmelt and after snowmelt. The coupling strength is strongest during snowmelt. The coupling strength after snowmelt, mainly due to hydrological effect (soil moisture impact), is stronger than coupling strength due to albedo effect (radiative impact) before snowmelt. A conceptual model is proposed to explain the mechanisms behind the spatial distribution of coupling strength.

Quantification of global land evapotranspiration (ET) has long been associated with large uncertainties due to the lack of reference observations. Several recently developed products now provide the capacity to estimate ET at global scales. These products, partly based on observational data, include satellite ]based products, land surface model (LSM) simulations, atmospheric reanalysis output, estimates based on empirical upscaling of eddycovariance flux measurements, and atmospheric water balance datasets. The LandFlux-EVAL project aims to evaluate and compare these newly developed datasets. Additionally, an evaluation of IPCC AR4 global climate model (GCM) simulations is presented, providing an assessment of their capacity to reproduce flux behavior relative to the observations ]based products. Though differently constrained with observations, the analyzed reference datasets display similar large-scale ET patterns. ET from the IPCC AR4 simulations was significantly smaller than that from the other products for India (up to 1 mm/d) and parts of eastern South America, and larger in the western USA, Australia and China. The inter-product variance is lower across the IPCC AR4 simulations than across the reference datasets in several regions, which indicates that uncertainties may be underestimated in the IPCC AR4 models due to shared biases of these simulations.

International audience; A global intercomparison of 12 monthly mean land surface heat flux products for the period 1993-1995 is presented. The intercomparison includes some of the first emerging global satellite-based products (developed at Paris Observatory, Max Planck Institute for Biogeochemistry, University of California Berkeley, University of Maryland, and Princeton University) and examples of fluxes produced by reanalyses (ERA-Interim, MERRA, NCEP-DOE) and off-line land surface models (GSWP-2, GLDAS CLM/Mosaic/Noah). An intercomparison of the global latent heat flux (Qle) annual means shows a spread of ˜20 W m-2 (all-product global average of ˜45 W m-2). A similar spread is observed for the sensible (Qh) and net radiative (Rn) fluxes. In general, the products correlate well with each other, helped by the large seasonal variability and common forcing data for some of the products. Expected spatial distributions related to the major climatic regimes and geographical features are reproduced by all products. Nevertheless, large Qle and Qh absolute differences are also observed. The fluxes were spatially averaged for 10 vegetation classes. The larger Qle differences were observed for the rain forest but, when normalized by mean fluxes, the differences were comparable to other classes. In general, the correlations between Qle and Rn were higher for the satellite-based products compared with the reanalyses and off-line models. The fluxes were also averaged for 10 selected basins. The seasonality was generally well captured by all products, but large differences in the flux partitioning were observed for some products and basins.

International audience; An overview of initiatives that are a part of the Global Land Atmosphere System Study (GLASS), which has ushered in an era in which land surface models (LSM) for numerical weather and climate prediction now incorporate complex vegetation responses, detailed hydrology, dynamic snowpack evolution, urban processes, and more, is presented. A critical goal of GLASS is to expand from the uncoupled point-based (PILPS) and globally based (GSWP) evaluations to include simulations that are fully coupled with the atmosphere. GLASS includes Global Soil Wetness Project Phase (GSWP) in which the earlier GSWP-2 datasets will be extended forward to the present, enabling scientific progress toward attribution of recent changes to various components of the climate system, including the terrestrial component.

Land surface models are typically constrained by one or a few observed variables, while assuming that the internal water and energy partitioning is sensitive to those observed variables and realistic enough to simulate unobserved variables. To verify these assumptions, in situ soil climate analysis network (SCAN) observations in the Lower Mississippi Basin (2002-2008) are analysed to quantify water and energy budget components and they are compared to Community Land Model (CLM3·5) simulations. The local soil texture is identified as a major indicator for water storage characteristics and the Normalized Difference Vegetation Index shows potential as a drought indicator in summer months. Both observations and simulations indicate a regime where, except in some summer months, evapotranspiration controls soil moisture. CLM simulations with different soil texture assignments show discharge sensitivity to soil moisture, but almost no impact on evapotranspiration and other energy balance components. The observed and simulated water budgets show a similar partitioning. However, the SCAN observed water balance does not close because of precipitation measurement errors, unobserved irrigation, lack of specific storage change measurements and errors in the computed actual evapotranspiration. The simulated heat flux partitioning differs from that 'observed', with a larger (resp. smaller) fraction of net radiation being used by latent (resp. sensible) heat flux, and unobserved freeze and thaw events. The comparison between observations and model simulations suggests that a consistent observation collection for multiple variables would be needed to constrain and improve the full set of land surface variable estimates. Copyright © 2010 John Wiley & Sons, Ltd. ispartof: Hydrological Processes vol:25 issue:4 pages:634-649 status: published

[1] Two different Atmospheric General Circulation Models (AGCMs), each coupled to three different land surface schemes (LSSs) (six different model configurations in total), are used to study the roles of different model components and different action processes in land-atmosphere coupling. Experiments show that, for the six model configurations, the choice of AGCMs is the main reason for the substantially different precipitation variability, predictability, and land-atmosphere coupling strength among the configurations. The impact of different LSSs is secondary. Intraseasonal precipitation variability, which is mainly a property of the AGCM, can impact land-atmosphere coupling both directly in the atmosphere and indirectly through soil moisture response to precipitation. These results lead to a common conceptual decomposition of the land-atmosphere coupling strength and increases the understanding on large-scale land-atmosphere coupling.

[1] The second phase of the Global Land-Atmosphere Coupling Experiment (GLACE-2) is aimed at quantifying, with a suite of long-range forecast systems, the degree to which realistic land surface initialization contributes to the skill of subseasonal precipitation and air temperature forecasts. Results, which focus here on North America, show significant contributions to temperature prediction skill out to two months across large portions of the continent. For precipitation forecasts, contributions to skill are much weaker but are still significant out to 45 days in some locations. Skill levels increase markedly when calculations are conditioned on the magnitude of the initial soil moisture anomaly.

[1] This paper analyzes and attributes spatial and temporal patterns of changes in the diurnal cycle of land surface air temperature in 20 simulations from 11 global coupled atmosphere-ocean general circulation models during the 20th century and the 21st century under the SRES A1B scenario. Most of the warming in the maximum (Tmax) and minimum(Tmin)temperaturesfrom1900to2099isattributed to enhanced surface downward longwave radiation (DLW), while changes in surface downward shortwave radiation (DSW) and cloud cover mainly contribute to the simulated decrease in the diurnal temperature range (DTR). Although the simulated DTR decreases are much smaller than the observed during the 20th century, the models unanimously predict substantial warming in both Tmax and Tmin and decreases in DTR, especially in high latitudes during the 21st century, in response to enhanced global-scale anthropogenic forcings (particularly greenhouse effects of atmospheric water vapor and in part aerosol radiative cooling in the tropics) and increased cloudiness inhigh latitudes. Citation: Zhou, L., R. E. Dickinson, P. Dirmeyer, A. Dai, and S.-K. Min (2009), Spatiotemporal patterns of changes in maximum and minimum temperatures in multi-model simulations,Geophys.Res.Lett.,36, L02702, doi:10.1029/2008GL036141.

[1] Three different land surface models (Hydrological improvements to the Simplified version of the Simple Biosphere model (HySSiB), Noah model, and Community Land Model (CLM)) were simulated on the NASA Goddard Space Flight Center's Land Information System platform at 1-km resolution over the Little River Experimental Watershed, Georgia, and the simulated results were analyzed to address the local-scale land-atmosphere processes. All the three models simulated the soil moisture in space and time realistically. The Noah model produced higher soil moisture whereas the CLM got lower soil moisture with many dry down phases. CLM and HySSiB models were oversensitive to the atmospheric events. Different vertical discretizations of the model layers affected the soil moisture results in all the three models. The arithmetic model ensemble mean soil moisture performed reasonably well even at individual in-situ measurement sites. We found that different model schemes partitioned the incoming water and energy differently and hence produced different results for the water and energy budget parameters. In CLM, the energy and water budget parameters were very closely connected to the soil moisture (e.g., evaporation, latent, and sensible heat) change. HySSiB produced very high surface runoff and very low subsurface runoff. The Noah model did not produce much surface and subsurface runoff resulting in high surface soil moisture. We did not find much variability in Noah latent heat, sensible heat, and ground heat fluxes. From soil moisture data assimilation point of view, the mean bias removed Noah soil moisture was found to be better than other data sets.

[1] By analyzing the data from the Second Global Soil Wetness Project (GSWP-2) sensitivity experiments, this study quantifies the sensitivities of soil wetness simulation to uncertainties in precipitation and radiation forcings and estimates the actual influence of these uncertainties. The sensitivity of soil moisture to uncertainties in radiation has a large seasonal cycle because of its temperature dependence. Sensitivity to uncertainties in precipitation is less temporally variable and is large in semiarid regions. Precipitation sensitivity is generally dominant over radiation sensitivity in cold climate, while in warm climate radiation sensitivity is dominant or both of them are high. However, actual uncertainties in precipitation are typically much larger than for net radiation in warm climate, so the practical impact is dominated by our lack of accurate precipitation estimates. Precipitation uncertainties account for about 2/3 of the total soil wetness uncertainties from all forcing data, while radiation uncertainties only account for about 1/6.

[1] Pronounced changes in land use and surface properties like those that have occurred in the Sahel could lead to reduced land surface emissivity and thus might have an asymmetric impact on daytime and nighttime surface air temperature. This paper analyzes the sensitivity of simulated climate and energy balance to changes in soil emissivity over the Sahel using the recently developed Community Land Model/Community Atmosphere Model. Model simulations indicate that the reduction of soil emissivity warms minimum temperature (Tmin) much faster than maximum temperature (Tmax) and thus decreases the diurnal temperature range (DTR) significantly. Lower emissivity reduces the outgoing longwave radiation and thus provides more energy to the atmosphere through increasing of sensible heat flux, ground and surface air temperatures. Statistical results show that, on average, a decrease of soil emissivity of 0.1 will increase Tmin by 0.55°C and 0.41°C, and decrease DTR by 0.59°C and 0.46°C under clear-sky and all-sky conditions, respectively, while Tmax changes little. The warming in Tmin and the decrease in DTR are much stronger during the dry season than the wet season and are higher in clear-sky conditions than all-sky conditions. These results suggest that changes in land surface emissivity over some particular regions might explain part of the observed decrease in DTR, especially over semi-arid regions.

A newly developed Atmospheric General Circulation Model (AGCM) at T62 spectral truncation with 28 terrainfollowing ( σ = p / p s ) levels coupled to the Modular Ocean Model version 3.0 (MOM3.0) is evaluated for its simulation of El Nino and the Southern Oscillation (ENSO). It is also compared with an older version of the AGCM coupled to the same ocean model. A dozen features of ENSO are validated. These characteristics of ENSO highlight its influence on global climate at seasonal to interannual scales. The major improvements of the ENSO simulation from this new coupled climate model are the seasonal phase locking of the ENSO variability to a realistic annual cycle of the eastern equatorial Pacific Ocean, the duration of the ENSO events and its evolution that is comparable to the ocean data assimilation. The two apparent drawbacks of this new model are its relatively weak ENSO variability and the presence of erroneous split ITCZ. The improvement of the ENSO simulation in the new coupled model is attributed to realistic thermocline variability and wind stress simulation.

[1] We report herein the publication and evaluation of the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II global interdisciplinary data record. The record consists of 52 data sets, with a common series in the 10-year period 1985 to 1996. Selected data series extend well beyond this period. All series are coregistered to a common grid and gap-filled for continuity using uniform procedures. We describe briefly the individual data sets within the collection; provide user guidance; and contrast, compare and evaluate those data sets containing similar parameters (land cover, NDVI, albedo, precipitation and near-surface meteorology). We also describe the process used to develop the Initiative II collection which involved a broad international scientific community focused on addressing a well-defined set of carbon, water and energy cycle questions within the context of a specific set of analysis tools. The communities that drove the definition of the Initiative II collection were investigators within the international scientific communities of the Global Energy and Water cycle Experiment, GEWEX, program (http://www.gewex.org/); the International Geosphere/Biosphere Program IGBP (http://www.igbp.kva.se); and the U.S. Global Change Research Program, USGCRP (http://www.usgcrp.gov/). Finally, we report usage statistics based on access and download of files from the ISLSCP Initiative II collection available at http://www.daac.ornl.gov.

[1] The quality of meteorological forcing data has a strong impact on the simulation of the land surface component of the hydrological cycle. In this paper, the sensitivity of soil moisture simulations to combinations of different external meteorological forcing and vegetation parameters supplied by the International Satellite Land-Surface Climatology Project (ISLSCP) Initiative II (II2) as part of the Second Global Soil Wetness Project (GSWP-2) is evaluated by using the SSiB land surface model. The simulated plant-available soil moisture in the top 1 m of soil is compared against in situ observations over grasslands and agricultural regions in the former Soviet Union, United States (Illinois), China, and Mongolia from the Global Soil Moisture Data Bank. It is found that the skill of the simulations is very sensitive to the source of meteorological forcing and that hybridization of reanalysis products with observational data substantially improves the soil moisture simulations compared to reanalysis data alone. Sensitivity is highest among products of precipitation, radiation, and vegetation class. The range of changes in the skill of soil moisture simulations with the same land surface model in 13 different sensitivity studies is as large as that resulting from 11 different land surface models driven with the same meteorological forcing from the baseline integrations of GSWP-2. Assuming differences among versions of meteorological forcing fields are indicative of uncertainties in our knowledge of these drivers of the land surface climate, the impact of that uncertainty on land surface hydrology is as large as that from the variations among land surface models.

[1] We have applied a unique water vapor tracing algorithm using observed precipitation and atmospheric analyses for the period 1979–2003 to estimate water budgets and recycling ratio (the fraction of precipitation over a region that originated as evaporation from the same region) over land areas across the globe. Over most mid- and high-latitude areas, a strong annual cycle of recycling ratio exists; low during winter when storm tracks are active, tropospheric circulation strong, and surface evaporation rates low, high during summer when winds are light and evaporation is greater. Trends in recycling ratio have been found over large areas at high-latitudes that are consistent with an expansion into spring of the warm-season regime of water vapor recycling. These trends are consistent with observed vegetation-related changes often attributed to global climate change, and are most evident over northern Europe and North America where the density of meteorological data influencing the atmospheric analyses is high. Less extensive trends are found in other seasons.

[1] The fields of 2-m temperature, relative humidity, precipitation, downward short-wave and long-wave radiation, net radiation, and sensible and latent heat flux from the ERA40 and NCEP/DOE reanalyses are compared with each other and with other independent ISLSCP-II data sets, where available. There are differences in the climatologies of the different data sets, but generally, they show consistent patterns of the major seasonal anomaly fields. The anomaly patterns are coherent, showing warm, dry seasonal biases associated with reduced precipitation and cloudiness, and the converse. This confirms that major changes in the atmospheric circulation patterns, with the associated differences in surface temperature, humidity, precipitation, cloud fields and incoming surface SW and LW radiation fluxes are captured by both reanalyses and the comparison ISLSCP-II data sets.

[1] Eighteen years of seasonal 10-member ensemble simulations have been performed with observed sea surface temperature (SST) to assess the ability of dynamical models to predict seasonal-interannual climate variations during boreal summer. In addition, test cases have been designed to assess the role in climate trends, predictability of land initial conditions, systematic errors of precipitation and radiation fluxes at the land surface, and increasing CO2. The model reproduces a global warming trend in surface temperature similar to that observed. This appears to be attributable to the influence of SST. However, surface flux replacement and atmospheric initialization can reduce the SST-driven trend even while improving the spatial pattern of surface air temperature. The results also show that land surface interaction has an effect both on surface temperature and the higher levels of the troposphere. However, realistically increasing CO2 concentrations have little impact in these seasonal simulations.

Land Surface Matter in Climate and Weather: The Climate near the Ground The Regional Climate The Global Climate The Sahelian Climate The Amazonian Climate The Boreal Climate The Asian Monsoon Climate.- How Measurable is the Earth System: The Energy Balance Closure Problem Radiation Measurements in Integrated Terrestrial Experiments Surface Turbulent Fluxes Accuracy and Utility of Aircraft Flux Measurements Boundary Layer Budgeting Vegetation Structure, Dynamics and Physiology Remote Sensing and Land Surface Experiments The Water Balance Concept Use of Field Experiments in Improving the Land Surface Description in Atmospheric Models Further Insight from Large-scale Observational Studies of Land/Atmosphere Interactions.- The Value of Land Surface Data Consolidation: Motivation for Data Consolidation Existing Degrees of Consolidation Achieving Full Consolidation Terrestrial Data Assimilation.- The Integrity of River and Drainage Basin Systems: Responses of Hydrological Processes to Environmental Change at Small Catchment Scales River Basin Responses to Global Change and Anthropogenic Impacts Responses of Continental Aquatic Systems at the Global Scale Case Study 1 - The Amazon Basin Case Study 2: The Elbe River Basin in Central Europe Case Study 3: The Mixed Underdeveloped/Developed Mgeni Catchment, South Africa Scaling Relative Responses of Terrestrial Aquatic Systems to Global Changes.- How to Evaluate Vulnerability in Changing Environmental Conditions: Predictability and Uncertainty Contrast between Predictive and Vulnerability Approaches The Scenario Approach The Vulnerability Approach Case Studies Conclusions.