Vasubandhu Misra

Dynamic Downscaling of Seasonal Simulations over South America

In this paper multiple atmospheric global circulation model (AGCM) integrations at T42 spectral truncation and prescribed sea surface temperature were used to drive regional spectral model (RSM) simulations at 80-km resolution for the austral summer season (January‐February‐March). Relative to the AGCM, the RSM improves the ensemble mean simulation of precipitation and the lower- and upper-level tropospheric circulation over both tropical and subtropical South America and the neighboring ocean basins. It is also seen that the RSM exacerbates the dry bias over the northern tip of South America and the Nordeste region, and perpetuates the erroneous split intertropical convergence zone (ITCZ) over both the Pacific and Atlantic Ocean basins from the AGCM. The RSM at 80-km horizontal resolution is able to reasonably resolve the Altiplano plateau. This led to an improvement in the mean precipitation over the plateau. The improved resolution orography in the RSM did not substantially change the predictability of the precipitation, surface fluxes, or upper- and lower-level winds in the vicinity of the Andes Mountains from the AGCM. In spite of identical convective and land surface parameterization schemes, the diagnostic quantities, such as precipitation and surface fluxes, show significant differences in the intramodel variability over oceans and certain parts of the Amazon River basin (ARB). However, the prognostic variables of the models exhibit relatively similar model noise structures and magnitude. This suggests that the model physics are in large part responsible for the divergence of the solutions in the two models. However, the surface temperature and fluxes from the land surface scheme of the model [Simplified Simple Biosphere scheme (SSiB)] display comparable intramodel variability, except over certain parts of ARB in the two models. This suggests a certain resilience of predictability in SSiB (over the chosen domain of study) to variations in horizontal resolution. It is seen in this study that the summer precipitation over tropical and subtropical South America is highly unpredictable in both models.

A proxy for high-resolution regional reanalysis for the Southeast United States: assessment of precipitation variability in dynamically downscaled reanalyses

We present an analysis of the seasonal, subseasonal, and diurnal variability of rainfall from COAPS Land–Atmosphere Regional Reanalysis for the Southeast at 10-km resolution (CLARReS10). Most of our assessment focuses on the representation of summertime subseasonal and diurnal variability. Summer precipitation in the Southeast United States is a particularly challenging modeling problem because of the variety of regional-scale phenomena, such as sea breeze, thunderstorms and squall lines, which are not adequately resolved in coarse atmospheric reanalyses but contribute significantly to the hydrological budget over the region. We find that the dynamically downscaled reanalyses are in good agreement with station and gridded observations in terms of both the relative seasonal distribution and the diurnal structure of precipitation, although total precipitation amounts tend to be systematically overestimated. The diurnal cycle of summer precipitation in the downscaled reanalyses is in very good agreement with station observations and a clear improvement both over their “parent” reanalyses and over newer-generation reanalyses. The seasonal cycle of precipitation is particularly well simulated in the Florida; this we attribute to the ability of the regional model to provide a more accurate representation of the spatial and temporal structure of finer-scale phenomena such as fronts and sea breezes. Over the northern portion of the domain summer precipitation in the downscaled reanalyses remains, as in the “parent” reanalyses, overestimated. Given the degree of success that dynamical downscaling of reanalyses demonstrates in the simulation of the characteristics of regional precipitation, its favorable comparison to conventional newer-generation reanalyses and its cost-effectiveness, we conclude that for the Southeast United states such downscaling is a viable proxy for high-resolution conventional reanalysis.

How Much Do Different Land Models Matter for Climate Simulation? Part I: Climatology and Variability

Abstract An atmospheric general circulation model (AGCM) is coupled to three different land surface schemes (LSSs), both individually and in combination (i.e., the LSSs receive the same AGCM forcing each time step and the averaged upward surface fluxes are passed back to the AGCM), to study the uncertainty of simulated climatologies and variabilities caused by different LSSs. This tiling of the LSSs is done to study the uncertainty of simulated mean climate and climate variability caused by variations between LSSs. The three LSSs produce significantly different surface fluxes over most of the land, no matter whether they are coupled individually or in combination. Although the three LSSs receive the same atmospheric forcing in the combined experiment, the inter-LSS spread of latent heat flux can be larger or smaller than the individually coupled experiment, depending mostly on the evaporation regime of the schemes in different regions. Differences in precipitation are the main reason for the different lat...

Reconciling the Spatial Distribution of the Surface Temperature Trends in the Southeastern United States

AbstractThis study attempts to explain the considerable spatial heterogeneity in the observed linear trends of monthly mean maximum and minimum temperatures (Tmax and Tmin) from station observations in the southeastern (SE) United States (specifically Florida, Alabama, Georgia, South Carolina, and North Carolina). In a majority of these station sites, the warming trends in Tmin are stronger in urban areas relative to rural areas. The linear trends of Tmin in urban areas of the SE United States are approximately 7°F century−1 compared to about 5.5°F century−1 in rural areas. The trends in Tmax show weaker warming (or stronger cooling) trends with irrigation, while trends in Tmin show stronger warming trends. This functionality of the temperature trends with land features also shows seasonality, with the boreal summer season showing the most consistent relationship in the trends of both Tmax and Tmin. This study reveals that linear trends in Tmax in the boreal summer season show a cooling trend of about 0.5...

Anomaly Nesting: A Methodology to Downscale Seasonal Climate Simulations from AGCMs

In this paper a methodology is proposed to downscale coarse-resolution atmospheric general circulation model (AGCM) seasonal simulations. Anomaly nesting involves replacing the climatology of the driving AGCM with observed (in this case the National Centers for Environmental Prediction reanalysis) climatology at the lateral boundaries of the nested regional climate model (the regional spectral model). In this study the methodology is tested over South America and the neighboring ocean basins. A comparison of the austral summer seasonal simulation with the conventional way of nesting, namely driving the regional model with full AGCM forcing, reveals that substantial gains in the deterministic skill are realized through anomaly nesting. It is also shown that the high-frequency variance (at 3‐30- and 30‐40-day time scales) is more realistic from the anomaly nesting procedure.

Validating and understanding the ENSO simulation in two coupled climate models

Abstract A newly developed Atmospheric General Circulation Model (AGCM) at T62 spectral truncation with 28 terrainfollowing (σ=pps) levels coupled to the Modular Ocean Model version 3.0 (MOM3.0) is evaluated for its simulation of El Ni˜no 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.

A Diagnosis of the 1979–2005 Extreme Rainfall Events in the Southeastern United States with Isentropic Moisture Tracing

A detailed analysis is performed to better understand the interannual and subseasonal variability of moisture sources of major recent dry (1980, 1990, and 2000) and wet (1994, 2003, and 2005) June‐August (JJA) seasons in the southeastern United States. Wet (dry) JJAs show an increased (decreased) standard deviation of daily precipitation. Whereas most days during dry JJAs have little or no precipitation, wet JJAs contain more days with significant precipitation and a large increase of heavy (110 mm) precipitation days. At least two tropical cyclone/depression landfalls occur in the southeastern United States during wet JJAs, whereas none occur during dry JJAs. The trajectory analysis suggests significant local recycling of moisture, implying that land surface feedback has the potential to enhance (suppress) precipitation anomalies during a wet (dry) JJA. Remote moisture sources during heavy precipitation events are very similar between wet and dry JJAs. The distinction between wet and dry JJAs lies in the frequency of heavy precipitation events. During the wet JJAs, heavy precipitation events contribute to more than half of the JJA precipitation total.

Coupled Air, Sea, and Land Interactions of the South American Monsoon

The dominant interannual variation of the austral summer South American monsoon season (SAM) is associated with El Nino–Southern Oscillation (ENSO). Although this teleconnection provides a basis for the seasonal predictability of SAM, it is shown that the conventional tier-2 modeling approach of prescribing observed sea surface temperature (SST) is inappropriate to capture this teleconnection. Furthermore, such a forced atmospheric general circulation model (AGCM) simulation leads to degradation of the SAM precipitation variability. However, when the same AGCM is coupled to an ocean general circulation model to allow for coupled air–sea interactions, then this ENSO–SAM teleconnection is reasonably well simulated. This is attributed to the role of air–sea coupling in modulating the large-scale east–west circulation, especially associated with Nino-3 SST anomalies. It is also shown that the land–atmosphere feedback in the SAM domain as a result of the inclusion of air–sea coupling is more robust. As a consequence of this stronger land–atmosphere feedback the decorrelation time of the daily rainfall in the SAM region is prolonged to match more closely with the observed behavior. A subtle difference in the austral summer seasonal precipitation anomalies between that over the Amazon River basin (ARB) and the SAM core region is also drawn from this study in reference to the influence of the air–sea interaction. It is shown that the dominant interannual precipitation variability over the ARB is simulated both by the uncoupled and coupled (to OGCM) AGCM in contrast to that over the SAM core region in southeastern Brazil.

The Influence of Pacific SST Variability on the Precipitation over Southern Africa

This study is an analysis of AGCM model results to understand the dynamics of the response of precipitation over southern Africa (SA) to anomalies in the sea surface temperature (SST) over the Pacific Ocean. The pattern of interannual precipitation anomaly over SA and its temporal variations are quite similar in both the ensemble mean of the control (where AGCM is forced with observed SSTs in all ocean basins) and experimental runs (where AGCM is forced with seasonally varying climatological SST over the Pacific Ocean). However, the amplitude of the variability is found to be relatively reduced in the experimental runs. This is shown to be a result of the modulation of the Walker circulation by the variability of Pacific Ocean SST. The regional teleconnection pattern between the dominant mode of SA precipitation variability and SST anomalies over the eastern Indian Ocean is also influenced by the variations in Pacific SST. The nature of the teleconnection between SA precipitation and eastern Indian SST is apparent only when the Pacific SST variability is excluded. This is corroborated from observations as well.

Hindcast skill and predictability for precipitation and two-meter air temperature anomalies in global circulation models over the Southeast United States

This paper presents an assessment of the seasonal prediction skill of current global circulation models, with a focus on the two-meter air temperature and precipitation over the Southeast United States. The model seasonal hindcasts are analyzed using measures of potential predictability, anomaly correlation, Brier skill score, and Gerrity skill score. The systematic differences in prediction skill of coupled ocean–atmosphere models versus models using prescribed (either observed or predicted) sea surface temperatures (SSTs) are documented. It is found that the predictability and the hindcast skill of the models vary seasonally and spatially. The largest potential predictability (signal-to-noise ratio) of precipitation anywhere in the United States is found in the Southeast in the spring and winter seasons. The maxima in the potential predictability of two-meter air temperature, however, reside outside the Southeast in all seasons. The largest deterministic hindcast skill over the Southeast is found in wintertime precipitation. At the same time, the boreal winter two-meter air temperature hindcasts have the smallest skill. The large wintertime precipitation skill, the lack of corresponding two-meter air temperature hindcast skill, and a lack of precipitation skill in any other season are features common to all three types of models (atmospheric models forced with observed SSTs, atmospheric models forced with predicted SSTs, and coupled ocean–atmosphere models). Atmospheric models with observed SST forcing demonstrate a moderate skill in hindcasting spring-and summertime two-meter air temperature anomalies, whereas coupled models and atmospheric models forced with predicted SSTs lack similar skill. Probabilistic and categorical hindcasts mirror the deterministic findings, i.e., there is very high skill for winter precipitation and none for summer precipitation. When skillful, the models are conservative, such that low-probability hindcasts tend to be overestimates, whereas high-probability hindcasts tend to be underestimates.