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

This study investigates the impacts of historical land-cover change on summer afternoon precipitation over North America using the Community Earth System Model. Using land–atmosphere coupling metrics, this study examines the sensitivity of afternoon atmospheric conditions to morning land surface states and fluxes that are altered by land-cover changes before and since 1850. The deforestation in the eastern United States prior to 1850 leads to increased latent but decreased sensible heat flux during the morning and a reduction in afternoon precipitation over the southern regions of the U.S. East Coast. The agricultural expansion over the Great Plains since preindustrial times shows similar effects on surface fluxes but results in a significant widespread increase in precipitation over the crop area. The coupling metrics exhibit a strong positive soil moisture–precipitation relationship over the Great Plains. Impacts of land-cover change on precipitation manifest through changes in rainfall frequency, rather than intensity, that are largely controlled by the distribution of CAPE as the trigger of convective precipitation. However, deforestation and later reforestation over the eastern United States, where coupling properties are different than the Great Plains, do not have as dominant an effect on afternoon precipitation. Additionally, precipitation over parts of the U.S. Southwest decreases in this model during the earlier period of East Coast deforestation, owing to changes in the large-scale circulation over North America driven by land-use changes prior to 1850.

Abstract

This observationally based study demonstrates the importance of the delayed hydrological response of snow cover and snowmelt over the Eurasian region and Tibet for variability of Indian summer monsoon rainfall during the first two months after onset. Using snow cover fraction and snow water equivalent data during 1967–2003, it is demonstrated that, although the snow-albedo effect is prevalent over western Eurasia, the delayed hydrological effect is strong and persistent over the eastern part. Long soil moisture memory and strong sensitivity of surface fluxes to soil moisture variations over eastern Asia and Tibet provide a mechanism for soil moisture anomalies generated by anomalies in winter and spring snowfall to affect rainfall during the initial months in summer. Dry soil moisture anomalies over the eastern Eurasian region associated with anomalous heating at the surface and midtroposphere help in anchoring of an anomalous upper-tropospheric “blocking” ridge around 100°E and its persistence. This not only leads to prolonged weakening of the subtropical westerly jet but also shifts its position southward of 30°N, followed by penetration of anomalous troughs in the westerlies into the Indian region. Simultaneously, intrusion of cold and dry air from the midlatitudes can reduce the convective instability and hence rainfall over India after the onset. Such a southward shift of the jet can also significantly weaken the vertical easterly wind shear over the Indian region in summer and lead to decrease in rainfall. This delayed hydrological effect also has the potential to modulate the snow–atmosphere coupling strength for temperature and precipitation in operational forecast models through soil moisture–evaporation–precipitation feedbacks.

Abstract

When initial soil moisture is perturbed among ensemble members in the operational NWS global forecast model, surface latent and sensible fluxes are immediately affected much more strongly, systematically, and over a greater area than conventional land–atmosphere coupling metrics suggest. Flux perturbations are likewise transmitted to the atmospheric boundary layer more formidably than climatology-based metrics would indicate. Impacts are not limited to the traditional land–atmosphere coupling hot spots, but extend over nearly all ice-free land areas of the globe. Key to isolating this effect is that initial atmospheric states are identical among quantities correlated, pinpointing soil moisture and snow cover. A consequence of this high sensitivity is that significant positive impacts of realistic land surface initialization on the skill of deterministic near-surface temperature and humidity forecasts are also immediate and nearly universal during boreal spring and summer (the period investigated) and persist for at least 3 days over most land areas. Land surface initialization may be more broadly important for weather forecasts than previously realized, as the research focus historically has been on subseasonal-to-seasonal time scales. This study attempts to bridge the gap between climate studies with their associated coupling assessments and weather forecast time scales. Furthermore, errors in land surface initialization and shortcomings in the parameterization of atmospheric processes sensitive to surface fluxes may have greater consequences than previously recognized, the latter exemplified by the lack of impact on precipitation forecasts even though the simulation of boundary layer development is shown to be greatly improved with realistic soil moisture initialization.

Abstract

The potential role of the land surface state in improving predictions of seasonal climate is investigated with a coupled land–atmosphere climate model. Climate simulations for 18 boreal-summer seasons (1982–99) have been conducted with specified observed sea surface temperature (SST). The impact on prediction skill of the initial land surface state (interannually varying versus climatological soil wetness) and the effect of errors in downward surface fluxes (precipitation and longwave/shortwave radiation) over land are investigated with a number of parallel experiments. Flux errors are addressed by replacing the downward fluxes with observed values in various combinations to ascertain the separate roles of water and energy flux errors on land surface state variables, upward water and energy fluxes from the land surface, and the important climate variables of precipitation and near-surface air temperature.

Large systematic errors are found in the model, which are only mildly alleviated by the specification of realistic initial soil wetness. The model shows little skill in simulating seasonal anomalies of precipitation, but it does have skill in simulating temperature variations. Replacement of the downward surface fluxes has a clear positive impact on systematic errors, suggesting that the land–atmosphere feedback is helping to exacerbate climate drift. Improvement in the simulation of year-to-year variations in climate is even more evident. With flux replacement, the climate model simulates temperature anomalies with considerable skill over nearly all land areas, and a large fraction of the globe shows significant skill in the simulation of precipitation anomalies. This suggests that the land surface can communicate climate anomalies back to the atmosphere, given proper meteorological forcing. Flux substitution appears to have the largest benefit to improving precipitation skill over the Northern Hemisphere midlatitudes, whereas use of realistic land surface initial conditions improves skill to significant levels over regions of the Southern Hemisphere. Correlations between sets of integrations show that the model has a robust and systematic global response to SST anomalies.

ABSTRACT

Recent studies have shown the impacts of historical land-use land-cover changes (i.e., deforestation) on hot temperature extremes; contradictory temperature responses have been found between studies using observations and climate models. However, different characterizations of surface temperature are sometimes used in the assessments: land surface skin temperature T _s is more commonly used in observation-based studies while near-surface air temperature T _2m is more often used in model-based studies. The inconsistent use of temperature variables is not inconsequential, and the relationship between deforestation and various temperature changes can be entangled, which complicates comparisons between observations and model simulations. In this study, the responses in the diurnal cycle of summertime T _s and T _2m to deforestation are investigated using the Community Earth System Model. For the daily maximum, opposite responses are found in T _s and T _2m. Due to decreased surface roughness after deforestation, the heat at the land surface cannot be efficiently dissipated into the air, leading to a warmer surface but cooler air. For the daily minimum, strong warming is found in T _2m, which exceeds daytime cooling and leads to overall warming in daily mean temperatures. After comparing several climate models, we find that the models agree in daytime land surface (T _s ) warming, but different turbulent transfer characteristics produce discrepancies in T _2m. Our work highlights the need to investigate the diurnal cycles of temperature responses carefully in land-cover change studies. Furthermore, consistent consideration of temperature variables should be applied in future comparisons involving observations and climate models.

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

Retrospective forecasts from CFSv2 are evaluated in terms of three elements of land–atmosphere coupling at subseasonal to seasonal time scales: sensitivity of the atmosphere to variations in land surface states, the magnitude of variability of land states and fluxes, and the memory or persistence of land surface anomalies. The Northern Hemisphere spring and summer seasons are considered for the period 1982–2009. Ensembles are constructed from all available pairings of initial land and atmosphere/ocean states taken from the Climate Forecast System Reanalysis at the start of April, May, and June among the 28 years, so that the effect of initial land states on the evolving forecasts can be assessed. Finally, improvement and continuance of forecast skill derived from accurate land surface initialization is related to the three coupling elements. It is found that soil moisture memory is the most broadly important element for significant improvement of realistic land initialization on forecast skill. However, coupling strength manifested through the elements of sensitivity and variability are necessary to realize the potential predictability provided by memory of initial land surface anomalies. Even though there is clear responsiveness of surface heat fluxes, near-surface temperature, humidity, and daytime boundary layer development to variations in soil moisture over much of the globe, precipitation in CFSv2 is unresponsive. Failure to realize potential predictability from land surface states could be due to unfavorable atmospheric stability or circulation states; poor quality of what is considered realistic soil moisture analyses; and errors in the land surface model, atmospheric model, or their coupled interaction.

Abstract

Multimodel ensemble forecasting has been shown to offer a systematic improvement in the skill of climate prediction with atmosphere and ocean circulation models. However, little such work has been done for the land surface component, an important lower boundary for weather and climate forecast models. In this study, the authors examine and evaluate several methods of combining individual global soil wetness products from uncoupled land surface model calculations and coupled land–atmosphere model reanalyses to produce an ensemble analysis. Analyses are verified against observations from the Global Soil Moisture Data Bank (GSMDB) with skill measured by correlation coefficient and root-mean-square error (RMSE). A preliminary transferability study is conducted as well for investigating the feasibility of transferring ensemble regression parameters within two specific regions (Illinois and east-central China) and between these two regions of similar climate and land use. The results show that when sufficient validation data are available, one can use a seasonally dependent linear regression to improve the skill of any individual model simulation of soil wetness. Further improvements in skill can be achieved with more sophisticated ensembling methods, such as the regression-adjusted multimodel ensemble mean analysis and regression-adjusted multimodel analysis. However, all the ensembling schemes involving regression usually do not help improve the skill scores as far as the simulation of anomalies of soil wetness is concerned. In the absence of calibration data, the simple arithmetic ensemble mean across multiple soil wetness products generally does as well or better than the best individual model at any location in the representation of both soil wetness and its anomaly. Transferability from one subset of stations from the Illinois or east-central China dataset to another gives satisfactory results. However, results are poor when transferring regression weights between different regions, even with similar climate regimes and land cover. Such an exercise helps us to understand better the virtues and limitations of various ensembling techniques and enables progress toward creating an optimum, model-independent analysis from a practical point of view.

Abstract

Recent studies in the Global Land–Atmosphere Coupling Experiment (GLACE) established a framework to estimate the extent to which anomalies in the land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. Within this framework, a multiyear GLACE-type experiment is carried out with a coupled land–atmosphere general circulation model to examine the interannual variability of land–atmosphere coupling strength. Soil wetness with intermediate values are in the range at which rainfall generation, near-surface air temperature, and surface turbulent fluxes are most sensitive to soil moisture anomalies, and thus, land–atmosphere coupling strength peaks in this range. As a result, the “hot spots” with strong land–atmosphere coupling strength appear in regions with intermediate climatological soil wetness (e.g., transition zones between dry and wet climates), consistent with previous studies. Land–atmosphere coupling strength experiences significant year-to-year variation because of interannual variability of soil moisture and the local spatiotemporal evolution of hydrologic regime. Coupling strength over areas with dry (wet) climate is enhanced during wet (dry) years since the resultant soil wetness enters into the sensitive range from a relatively insensitive range, and soil moisture can have stronger potential impact on surface turbulent fluxes and convection. On the other hand, land–atmosphere coupling strength over areas with wet (dry) climate is weakened during wet (dry) years since the soil wetness moves further away from the sensitive range. This results in a positive correlation between the land–atmosphere coupling strength and soil moisture anomalies over areas with dry climate and a negative correlation over areas with wet climate.