Ants Leetmaa

Model Projections of an Imminent Transition to a More Arid Climate in Southwestern North America

How anthropogenic climate change will affect hydroclimate in the arid regions of southwestern North America has implications for the allocation of water resources and the course of regional development. Here we show that there is a broad consensus among climate models that this region will dry in the 21st century and that the transition to a more arid climate should already be under way. If these models are correct, the levels of aridity of the recent multiyear drought or the Dust Bowl and the 1950s droughts will become the new climatology of the American Southwest within a time frame of years to decades.

Weakening of tropical Pacific atmospheric circulation due to anthropogenic forcing

Since the mid-nineteenth century the Earths surface has warmed, and models indicate that human activities have caused part of the warming by altering the radiative balance of the atmosphere. Simple theories suggest that global warming will reduce the strength of the mean tropical atmospheric circulation. An important aspect of this tropical circulation is a large-scale zonal (east–west) overturning of air across the equatorial Pacific Ocean—driven by convection to the west and subsidence to the east—known as the Walker circulation. Here we explore changes in tropical Pacific circulation since the mid-nineteenth century using observations and a suite of global climate model experiments. Observed Indo-Pacific sea level pressure reveals a weakening of the Walker circulation. The size of this trend is consistent with theoretical predictions, is accurately reproduced by climate model simulations and, within the climate models, is largely due to anthropogenic forcing. The climate model indicates that the weakened surface winds have altered the thermal structure and circulation of the tropical Pacific Ocean. These results support model projections of further weakening of tropical atmospheric circulation during the twenty-first century.

A review of the predictability and prediction of ENSO

A hierarchy of El Nino-Southern Oscillation (ENSO) prediction schemes has been developed during the Tropical Ocean-Global Atmosphere (TOGA) program which includes statistical schemes and physical models. The statistical models are, in general, based on linear statistical techniques and can be classified into models which use atmospheric (sea level pressure or surface wind) or oceanic (sea surface temperature or a measure of upper ocean heat content) quantities or a combination of oceanic and atmospheric quantities as predictors. The physical models consist of coupled ocean-atmosphere models of varying degrees of complexity, ranging from simplified coupled models of the “shallow water” type to coupled general circulation models. All models, statistical and physical, perform considerably better than the persistence forecast in predicting typical indices of ENSO on lead times of 6 to 12 months. The TOGA program can be regarded as a success from this perspective. However, despite the demonstrated predictability, little is known about ENSO predictability limits and the predictability of phenomena outside the tropical Pacific. Furthermore, the predictability of anomalous features known to be associated with ENSO (e.g., Indian monsoon and Sahel rainfall, southern African drought, and off-equatorial sea surface temperature) needs to be addressed in more detail. As well, the relative importance of different physical mechanisms (in the ocean or atmosphere) has yet to be established. A seasonal dependence in predictability is seen in many models, but the processes responsible for it are not fully understood, and its meaning is still a matter of scientific discussion. Likewise, a marked decadal variation in skill is observed, and the reasons for this are still under investigation. Finally, the different prediction models yield similar skills, although they are initialized quite differently. The reasons for these differences are also unclear.

An Improved Coupled Model for ENSO Prediction and Implications for Ocean Initialization. Part I: The Ocean Data Assimilation System

Abstract An improved forecast system has been developed for El Nino–Southern Oscillation (ENSO) prediction at the National Centers for Environmental Prediction. Improvements have been made both to the ocean data assimilation system and to the coupled ocean–atmosphere forecast model. In Part I of a two-part paper the authors describe the new assimilation system. The important changes are 1) the incorporation of vertical variation in the first-guess error variance that concentrates temperature corrections in the thermocline and 2) the overall reduction in the magnitude of the estimated first-guess error. The new system was used to produce a set of retrospective ocean analyses for 1980–95. The new analyses are less noisy than their earlier counterparts and compare more favorably with independent measurements of temperature, currents, and sea surface height variability. Part II of this work presents the results of using these analyses to initialize the coupled forecast model for ENSO prediction.

An Ocean Analysis System for Seasonal to Interannual Climate Studies

Abstract A dynamical model-based ocean analysis system has been implemented at the National Meteorological Center (NMC). This is used to provide retrospective and routine weekly analyses for the Pacific and Atlantic Oceans. Retrospective analyses have been performed for the period mid-1982 to mid-1993. The analyses are used for diagnostics of past climatic variability, real-time climate monitoring, and as initial conditions for coupled multiseason forecasts. The assimilation system is based on optimal interpolation objective analysis solved using an equivalent variational formulation. Analysis errors are estimated by comparisons to independent datasets such as temperature data from moorings and sea level information from tide gauges. In the near equatorial zone rms errors in thermocline depth are of order of 6–15 m. Comparisons of sea level estimates from the reanalyses with the records from tide gauges indicate that the rms sea level errors for monthly analysis are of the order of 0.04–0.09 m. For the we...

Long-Lead Seasonal Forecasts—Where Do We Stand?

Abstract The National Weather Service intends to begin routinely issuing long-lead forecasts of 3-month mean U.S. temperature and precipitation by the beginning of 1995. The ability to produce useful forecasts for certain seasons and regions at projection times of upto 1 yr is attributed to advances in data observing and processing, computer capability, and physical understanding-particularly, for tropical ocean-atmosphere phenomena. Because much of the skill of the forecasts comes from anomalies of tropical SST related to ENSO, we highlight here long-lead forecasts of the tropical Pacific SST itself, which have higher skill than the U.S forecasts that are made largely on their basis. The performance of five ENSO prediction systems is examined: Two are dynamical [the Cane-Zebiak simple coupled model of Lamont-Doherty Earth Observatory and the nonsimpie coupled model of the National Centers for Environmental Prediction (NCEP)]; one is a hybrid coupled model (the Scripps Institution for Oceanography-Max Pla...

Teleconnective Response of the Pacific–North American Region Atmosphere to Large Central Equatorial Pacific SST Anomalies

Abstract A prominent year-round ensemble response to a global sea surface temperature (SST) anomaly field fixed to that for January 1992 (near the peak of a major warm El Nino–Southern Oscillation episode) was observed in a 20-yr integration of the general circulation model used for operational seasonal prediction by the U.S. National Weather Service. This motivated a detailed observational reassessment of the teleconnections between strong SST anomalies in the central equatorial Pacific Ocean and Pacific–North America region 700-hPa heights and U.S. surface temperatures and precipitation. The approach used consisted of formation of monthly mean composites formed separately from cases in which the SST anomaly in a key area of the central equatorial Pacific Ocean was either large and positive or large and negative. Extensive permutation tests were conducted to test null hypotheses of no signal in these composites. The results provided a substantial case for the presence of teleconnections to either the pos...

Relationships between Climate Variability and Winter Temperature Extremes in the United States

Abstract Time series representing two of the climate systems leading patterns of variability, namely El Nino–Southern Oscillation (ENSO) and the Arctic Oscillation (AO), are used together with 50 yr of daily mean surface air temperature data over the conterminous United States to diagnose relationships between winter temperature extremes and interannual climate variability. The aim is to focus attention on some of the physical phenomena that climate models must be able to simulate in order to be deemed credible for use in weather and climate forecasts and assessments. Since the 1950s there has been considerable decadal variability in winter surface air temperature extremes. At most locations in the United States the number of daily extremes is reduced during El Nino, and increased during La Nina and ENSO-neutral years. These changes are qualitatively consistent with a decrease in the daily mean surface air temperature variance during El Nino relative to La Nina and ENSO neutral. Changes in the number of w...

Assessing a GCM`s suitability for making seasonal predictions

Abstract This study investigates the predictability of seasonal mean circulation anomalies associated purely with the influence of anomalous sea surface temperatures (SSTs). Within this framework, seasonal mean atmospheric anomalies on a case by case basis are understood to consist of a potentially predictable boundary-forced component and an unpredictable naturally varying component. The predictive capability of an atmospheric general circulation model (AGCM) for seasonal timescales should therefore be assessed in terms of the average skill over many cases, since it is only then that the boundary-forced predictable signal in observations can be identified. To illustrate, experiments for 1982–1993 using two versions of an AGCM are presented. The models, referred to here as MRF8 and MRF9, differ in the parameterization of a single process. Each model is run nine times for the 12 years using different initial conditions but identical observed global SSTS. The nine-member ensemble mean anomalies for each sea...

Dominant Factors Influencing the Seasonal Predictability of U.S. Precipitation and Surface Air Temperature

Abstract The relative contributions of El Nino–Southern Oscillation (ENSO), long-term tropical Pacific variations, and the Arctic oscillation (AO) to the explained variance of U.S. precipitation and surface air temperature are investigated. The time variability of monthly precipitation in the tropical Pacific basin is separated into high-pass and low-pass filtered components. The leading EOFs of the high-pass and low-pass filtered data capture ENSO cycle–related interannual variability and ENSO-like interdecadal variability, respectively. The dominant mode of variability in the extratropics is the AO, which has been implicated in some of the secular variability of climate in the Northern Hemisphere extratropics. ENSO produces large, reasonably reproducible spatial and temporal shifts in tropical precipitation. The tropical interdecadal variability produces more subtle, but still significant, shifts in tropical precipitation that contribute significantly to the explained variance and to trends in the North...