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- Author or Editor: Paul Dirmeyer x
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Abstract
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 surface variability. The impacts of land surface variability on climate variability are clearer on monthly timescales than on synoptic timescales. Absolute measures of the impact of surface variability on the synoptic scale are not strong, but the time evolution of variability is consistent with expectations that the land surface does exert some control on climate variability.
Abstract
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 surface variability. The impacts of land surface variability on climate variability are clearer on monthly timescales than on synoptic timescales. Absolute measures of the impact of surface variability on the synoptic scale are not strong, but the time evolution of variability is consistent with expectations that the land surface does exert some control on climate variability.
Abstract
The Global Soil Wetness Project (GSWP) is an international land surface modeling research effort involving dataset production, validation, model comparison, and scientific investigation in the areas of land surface hydrology and climatology. GSWP is characterized by the integration of multiple land surface models on a latitude–longitude grid in a stand-alone uncoupled mode, driven by meteorological forcing data constructed by combining atmospheric analyses and gridded observed data products. The models produce time series of gridded estimates of land surface fluxes and state variables that are then studied and compared. Defining characteristics that have distinguished GSWP include its global scale, application of land surface models in the same gridded structure as they are used in weather and climate models, and the multimodel approach, which included production of a multimodel analysis in its second phase. This paper gives an overview of the history of GSWP beginning with its inception within the International Satellite Land Surface Climatology Project. Various phases of the project are described, and a review of scientific results stemming from the project is presented. Musings on future directions of research are also discussed.
Abstract
The Global Soil Wetness Project (GSWP) is an international land surface modeling research effort involving dataset production, validation, model comparison, and scientific investigation in the areas of land surface hydrology and climatology. GSWP is characterized by the integration of multiple land surface models on a latitude–longitude grid in a stand-alone uncoupled mode, driven by meteorological forcing data constructed by combining atmospheric analyses and gridded observed data products. The models produce time series of gridded estimates of land surface fluxes and state variables that are then studied and compared. Defining characteristics that have distinguished GSWP include its global scale, application of land surface models in the same gridded structure as they are used in weather and climate models, and the multimodel approach, which included production of a multimodel analysis in its second phase. This paper gives an overview of the history of GSWP beginning with its inception within the International Satellite Land Surface Climatology Project. Various phases of the project are described, and a review of scientific results stemming from the project is presented. Musings on future directions of research are also discussed.
Abstract
In this study of snow–atmosphere coupling strength, the previous snow–atmosphere coupled modeling experiment is extended to investigate the separate impacts on the atmosphere of the radiatively driven snow albedo effect and the snow hydrological effect that operates through soil moisture, evapotranspiration, and precipitation feedbacks. The albedo effect is governed by snow cover fraction, while the hydrological effect is controlled by anomalies in snow water equivalent. Realistic snow cover from satellite estimates is prescribed and compared with model-generated values to isolate the snow albedo effect. Similarly, imparting realistic snow water equivalent from the Global Land Data Assimilation System in the model allows for estimation of the snow hydrological effect. The snow albedo effect is found to be active before, and especially during, the snowmelt period, and regions of strong albedo-driven coupling move northward during spring, with the retreating edge of the snowpack in the Northern Hemisphere. The snow hydrological effect appears first during snowmelt and can persist for months afterward. The contributing factors to the snow albedo effect are analyzed in a theoretical framework.
Abstract
In this study of snow–atmosphere coupling strength, the previous snow–atmosphere coupled modeling experiment is extended to investigate the separate impacts on the atmosphere of the radiatively driven snow albedo effect and the snow hydrological effect that operates through soil moisture, evapotranspiration, and precipitation feedbacks. The albedo effect is governed by snow cover fraction, while the hydrological effect is controlled by anomalies in snow water equivalent. Realistic snow cover from satellite estimates is prescribed and compared with model-generated values to isolate the snow albedo effect. Similarly, imparting realistic snow water equivalent from the Global Land Data Assimilation System in the model allows for estimation of the snow hydrological effect. The snow albedo effect is found to be active before, and especially during, the snowmelt period, and regions of strong albedo-driven coupling move northward during spring, with the retreating edge of the snowpack in the Northern Hemisphere. The snow hydrological effect appears first during snowmelt and can persist for months afterward. The contributing factors to the snow albedo effect are analyzed in a theoretical framework.
Abstract
Skill in ensemble-mean dynamical seasonal climate hindcasts with a coupled land–atmosphere model and specified observed sea surface temperature is compared to that for long multidecade integrations of the same model where the initial conditions are far removed from the seasons of validation. The evaluations are performed for surface temperature and compared among all seasons. Skill is found to be higher in the seasonal simulations than in the multidecadal integrations except during boreal winter. The higher skill is prominent even beyond the first month when the direct influence of the atmospheric initial state elevates model skill. Skill is generally found to be lowest during the winter season for the dynamical seasonal forecasts. This is in contrast to the multiyear integrations, which show some of the highest skill during winter—as high as the dynamical seasonal forecasts. The reason for the differences in skill during the nonwinter months is attributed to the severe climate drift in the long simulations, manifested through errors in downward fluxes of water and energy over land and evident in soil wetness. The drift presses the land surface to extreme dry or wet states over much of the globe, into a range where there is little sensitivity of evaporation to fluctuations in soil moisture. Thus, the land–atmosphere feedback is suppressed, which appears to lessen the model's ability to respond correctly over land to remote ocean temperature anomalies. During winter the land surface is largely decoupled from the atmosphere due to increased baroclinic activity in the land-dominated Northern Hemisphere, while at the same time tropical ocean anomalies have their strongest influence. This combination of effects neutralizes the negative impact of climate drift over land during that season and puts all of the climate simulations on an equal footing.
Abstract
Skill in ensemble-mean dynamical seasonal climate hindcasts with a coupled land–atmosphere model and specified observed sea surface temperature is compared to that for long multidecade integrations of the same model where the initial conditions are far removed from the seasons of validation. The evaluations are performed for surface temperature and compared among all seasons. Skill is found to be higher in the seasonal simulations than in the multidecadal integrations except during boreal winter. The higher skill is prominent even beyond the first month when the direct influence of the atmospheric initial state elevates model skill. Skill is generally found to be lowest during the winter season for the dynamical seasonal forecasts. This is in contrast to the multiyear integrations, which show some of the highest skill during winter—as high as the dynamical seasonal forecasts. The reason for the differences in skill during the nonwinter months is attributed to the severe climate drift in the long simulations, manifested through errors in downward fluxes of water and energy over land and evident in soil wetness. The drift presses the land surface to extreme dry or wet states over much of the globe, into a range where there is little sensitivity of evaporation to fluctuations in soil moisture. Thus, the land–atmosphere feedback is suppressed, which appears to lessen the model's ability to respond correctly over land to remote ocean temperature anomalies. During winter the land surface is largely decoupled from the atmosphere due to increased baroclinic activity in the land-dominated Northern Hemisphere, while at the same time tropical ocean anomalies have their strongest influence. This combination of effects neutralizes the negative impact of climate drift over land during that season and puts all of the climate simulations on an equal footing.
Abstract
The role of the land surface in contributing to the potential predictability of the boreal summer climate is investigated with a coupled land–atmosphere climate model. Ensemble simulations for 1982–99 have been conducted with specified observed sea surface temperatures (SSTs). Several treatments of the land surface are investigated: climatological land surface initialization, realistic initialization of soil wetness, and a series of experiments where downward surface fluxes over land are replaced with observed proxies of precipitation, shortwave, and longwave radiation. Without flux replacement the model exhibits strong drift in soil wetness and both systematic errors and poor simulation of interannual variations of precipitation and near-surface temperature. With flux replacement there are large improvements in simulation of both spatial patterns and interannual variability of precipitation and surface temperature. The land surface apparently does contribute, through positive feedback with the atmosphere, to regional climate anomalies. However, because of the sizeable noise component in precipitation, the strong land–atmosphere feedback may not translate into reliable enhancements in predictability, particularly in years of weak anomalies in the land surface initial conditions at the start of boreal summer.
Abstract
The role of the land surface in contributing to the potential predictability of the boreal summer climate is investigated with a coupled land–atmosphere climate model. Ensemble simulations for 1982–99 have been conducted with specified observed sea surface temperatures (SSTs). Several treatments of the land surface are investigated: climatological land surface initialization, realistic initialization of soil wetness, and a series of experiments where downward surface fluxes over land are replaced with observed proxies of precipitation, shortwave, and longwave radiation. Without flux replacement the model exhibits strong drift in soil wetness and both systematic errors and poor simulation of interannual variations of precipitation and near-surface temperature. With flux replacement there are large improvements in simulation of both spatial patterns and interannual variability of precipitation and surface temperature. The land surface apparently does contribute, through positive feedback with the atmosphere, to regional climate anomalies. However, because of the sizeable noise component in precipitation, the strong land–atmosphere feedback may not translate into reliable enhancements in predictability, particularly in years of weak anomalies in the land surface initial conditions at the start of boreal summer.
Abstract
The impact of improvements in land surface initialization and specification of observed rainfall in global climate model simulations of boreal summer are examined to determine how the changes propagate around the hydrologic cycle in the coupled land–atmosphere system. On the global scale, about 70% of any imparted signal in the hydrologic cycle is lost in the transition from atmosphere to land, and 70% of the remaining signal is lost from land to atmosphere. This means that globally, less than 10% of the signal of any change survives the complete circuit of the hydrologic cycle in this model. Regionally, there is a great deal of variability. Specification of observed precipitation to the land component of the climate model strongly communicates its signal to soil wetness in rainy regions, but predictive skill in evapotranspiration arises primarily in dry regions. A maximum in signal transmission to model precipitation exists in between, peaking where mean rainfall rates are 1.5–2 mm day−1. It appears that the nature of the climate system inherently limits to these regions the potential impact on prediction of improvements in the ability of models to simulate the water cycle. Land initial conditions impart a weaker signal on the system than replacement of precipitation, so a weaker response is realized in the system, focused mainly in dry regions.
Abstract
The impact of improvements in land surface initialization and specification of observed rainfall in global climate model simulations of boreal summer are examined to determine how the changes propagate around the hydrologic cycle in the coupled land–atmosphere system. On the global scale, about 70% of any imparted signal in the hydrologic cycle is lost in the transition from atmosphere to land, and 70% of the remaining signal is lost from land to atmosphere. This means that globally, less than 10% of the signal of any change survives the complete circuit of the hydrologic cycle in this model. Regionally, there is a great deal of variability. Specification of observed precipitation to the land component of the climate model strongly communicates its signal to soil wetness in rainy regions, but predictive skill in evapotranspiration arises primarily in dry regions. A maximum in signal transmission to model precipitation exists in between, peaking where mean rainfall rates are 1.5–2 mm day−1. It appears that the nature of the climate system inherently limits to these regions the potential impact on prediction of improvements in the ability of models to simulate the water cycle. Land initial conditions impart a weaker signal on the system than replacement of precipitation, so a weaker response is realized in the system, focused mainly in dry regions.
Abstract
This is the first of a two-part article that investigates the impact of land surface evaporation variability on the interannual variability of precipitation and compares it with the impact caused by sea surface temperature variability. Previous works by Koster and Suarez and Koster et al. provide the general strategy to control oceanic and land surface evaporation. For this part of the study, their numerical experiments are repeated using the Center for Ocean–Land–Atmosphere Studies (COLA) general circulation model. However, emphasis is put on the dynamics of the response, including a discussion of the changes in the mean climate; in particular, it is observed that the suppressed land evaporation variability changes the mean Northern Hemisphere storm track and the mean position of the intertropical convergence zone, which in turn affect the mean precipitation.
The analysis of the precipitation variance reveals a general agreement with previous works for the midlatitudes, whereas in the Tropics a stronger land-induced signal is detected. Furthermore, important regional differences in the response are found. Specifically, there is a predominant land signal over the Amazon region, in contrast to an equivalence between the land and ocean forcings over the Congo basin region. Finally, the model appears to be slightly more sensitive to seasonal–interannual variations of the land forcing than is the one adopted by Koster et al.
Abstract
This is the first of a two-part article that investigates the impact of land surface evaporation variability on the interannual variability of precipitation and compares it with the impact caused by sea surface temperature variability. Previous works by Koster and Suarez and Koster et al. provide the general strategy to control oceanic and land surface evaporation. For this part of the study, their numerical experiments are repeated using the Center for Ocean–Land–Atmosphere Studies (COLA) general circulation model. However, emphasis is put on the dynamics of the response, including a discussion of the changes in the mean climate; in particular, it is observed that the suppressed land evaporation variability changes the mean Northern Hemisphere storm track and the mean position of the intertropical convergence zone, which in turn affect the mean precipitation.
The analysis of the precipitation variance reveals a general agreement with previous works for the midlatitudes, whereas in the Tropics a stronger land-induced signal is detected. Furthermore, important regional differences in the response are found. Specifically, there is a predominant land signal over the Amazon region, in contrast to an equivalence between the land and ocean forcings over the Congo basin region. Finally, the model appears to be slightly more sensitive to seasonal–interannual variations of the land forcing than is the one adopted by Koster et al.
Abstract
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 potential sources of the drift are proposed.
Abstract
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 potential sources of the drift are proposed.
Abstract
Snow–atmosphere coupling strength, the degree to which the atmosphere (temperature and precipitation) responds to underlying snow anomalies, is investigated using the Community Climate System Model (CCSM) with realistic snow information obtained from satellite and data assimilation. The coupling strength is quantified using seasonal simulations initialized in late boreal winter with realistic initial snow states or forced with realistic large-scale snow anomalies, including both snow cover fraction observed by remote sensing and snow water equivalent from land data assimilation. Errors due to deficiencies in the land model snow scheme and precipitation biases in the atmospheric model are mitigated by prescribing realistic snow states. The spatial and temporal distributions of strong snow–atmosphere coupling in this model are revealed to track the continental snow cover edge poleward during the ablation period in spring, with secondary maxima after snowmelt. Compared with prescribed “perfect” snow simulations, the free-running CCSM captures major regions of strong snow–atmosphere coupling strength, with only minor departures in magnitude, but showing uneven biases over the Northern Hemisphere. Signals of strong coupling to air temperature are found to propagate vertically into the troposphere, at least up to 500 hPa over the coupling “cold spots.” The main mechanism for this vertical propagation is found to be longwave radiation and condensation heating.
Abstract
Snow–atmosphere coupling strength, the degree to which the atmosphere (temperature and precipitation) responds to underlying snow anomalies, is investigated using the Community Climate System Model (CCSM) with realistic snow information obtained from satellite and data assimilation. The coupling strength is quantified using seasonal simulations initialized in late boreal winter with realistic initial snow states or forced with realistic large-scale snow anomalies, including both snow cover fraction observed by remote sensing and snow water equivalent from land data assimilation. Errors due to deficiencies in the land model snow scheme and precipitation biases in the atmospheric model are mitigated by prescribing realistic snow states. The spatial and temporal distributions of strong snow–atmosphere coupling in this model are revealed to track the continental snow cover edge poleward during the ablation period in spring, with secondary maxima after snowmelt. Compared with prescribed “perfect” snow simulations, the free-running CCSM captures major regions of strong snow–atmosphere coupling strength, with only minor departures in magnitude, but showing uneven biases over the Northern Hemisphere. Signals of strong coupling to air temperature are found to propagate vertically into the troposphere, at least up to 500 hPa over the coupling “cold spots.” The main mechanism for this vertical propagation is found to be longwave radiation and condensation heating.
Abstract
Land states can affect the atmosphere through their control of surface turbulent fluxes and the subsequent impact of those fluxes on boundary layer properties. Information theoretic (IT) metrics are ideal to study the strength and type of coupling between surface soil moisture (SM) and land surface heat fluxes (HFs) because they are nonparametric and thus appropriate for the analysis of highly complex Earth systems containing nonlinear cause-and-effect interactions that may have nonnormal distributions. Specifically, a methodology for the estimation of IT metrics from noisy time series is proposed, accounting for random errors in satellite-based SM data. Performance of the proposed method is demonstrated through synthetic tests. Efficacy of the method is greatest for estimates of entropy and mutual information involving SM; improvements to estimates of transfer entropy are significant but less stark. A global depiction of the information flow between SM and HFs is then constructed from observationally based gridded data. This is used as independent verification for two configurations of the ECMWF modeling system: unconstrained open-loop (retrospective forecasts) and constrained by data assimilation (ERA5). Compared to studies that only investigate the linear SM–HF relationships, extended regions of significant terrestrial coupling are found over the globe, as IT metrics enable detection of nonlinear dependencies. The magnitude and spatial variability of coupling strength and type from models show discrepancies with those from observations, highlighting the potential to improve SM and HF covariability within models. Although ERA5 did not perform better than the unconstrained model in very dry climates, its performance is generally superior to that of the unconstrained model across metrics.
Abstract
Land states can affect the atmosphere through their control of surface turbulent fluxes and the subsequent impact of those fluxes on boundary layer properties. Information theoretic (IT) metrics are ideal to study the strength and type of coupling between surface soil moisture (SM) and land surface heat fluxes (HFs) because they are nonparametric and thus appropriate for the analysis of highly complex Earth systems containing nonlinear cause-and-effect interactions that may have nonnormal distributions. Specifically, a methodology for the estimation of IT metrics from noisy time series is proposed, accounting for random errors in satellite-based SM data. Performance of the proposed method is demonstrated through synthetic tests. Efficacy of the method is greatest for estimates of entropy and mutual information involving SM; improvements to estimates of transfer entropy are significant but less stark. A global depiction of the information flow between SM and HFs is then constructed from observationally based gridded data. This is used as independent verification for two configurations of the ECMWF modeling system: unconstrained open-loop (retrospective forecasts) and constrained by data assimilation (ERA5). Compared to studies that only investigate the linear SM–HF relationships, extended regions of significant terrestrial coupling are found over the globe, as IT metrics enable detection of nonlinear dependencies. The magnitude and spatial variability of coupling strength and type from models show discrepancies with those from observations, highlighting the potential to improve SM and HF covariability within models. Although ERA5 did not perform better than the unconstrained model in very dry climates, its performance is generally superior to that of the unconstrained model across metrics.
