The Land-Sea Warming Contrast as the Driver of Tropical Sea Level Pressure Changes

In this presentation we address the causes of the large-scale tropical sea level pressure (SLP) changes during climate change. The analysis we present is based on climate change model simulations, observed trends and the seasonal cycle. In all three cases the regional changes of tropospheric temperature (Ttropos) and SLP are strongly related to each other. This relationship basically follows the Bjerknes Circulation Theorem, with relative low regional SLP where we have relative high Ttropos and vice versa. A simple physical model suggests a tropical SLP response to horizontally inhomogeneous warming in the tropical Ttropos, with a regression coefficient of about -1.7 hPa/K. This relationship explains a large fraction of observed and predicted changes in the tropical SLP. It is shown that in climate change model simulations the tropical land-sea warm-ing contrast, is the most significant structure in the regional Ttropos changes relative to the tropical mean changes. Since the land-sea warming contrast exist in the absent of any atmospheric circulation changes it can be argued that the large-scale response of tropical SLP changes is to first order a response to the tropical land-sea warming con-trast, with decreasing SLP over the sector of strongest warming (South America to Afri-ca) and increasing SLP elsewhere, which is roughly the Indo-Pacific warm pool region. A model intercomparison reveals that climate models with a strong land-sea contrast in surface temperature tend to have also a strong land-sea contrast in Ttropos and SLP. In an idealized land-sea contrast experiment a similar response of the SLP and Ttropos as in the climate change experiments can be found. As SLP changes and changes in atmospheric circulation go hand in hand, these results suggest an increase in the potential for deep convection conditions over the Atlantic Sector and a decrease over the Indo-Pacific warm pool region in the future

Comparing the spatial structure of variability in two datasets against each other on the basis of EOF-modes

In analysis of climate variability or change it is often of interest how the spatial structure in modes of variability in two datasets differ from each other, e.g. between past and future climate or between models and observations. Often such analysis is based on Empirical Orthogonal Function (EOF) analysis or other simple indices of large-scale spatial structures. The present analysis lays out a concept on how two datasets of multi-variate climate variability can be compared against each other on basis of EOF analysis and how the differences in the multi-variate spatial structure between the two datasets can be quantified in terms of explained variance in the leading spatial patterns. It is also illustrated how the patterns of largest differences between the two datasets can be defined and interpreted. We illustrate this method on the basis of several well-defined artificial examples and by comparing our approach with examples of climate change studies from the literature. These literature examples include analysis of changes in the modes of variability under climate change for the Sea Level Pressure (SLP) of the North Atlantic and Europe, the SLP of the Southern Hemisphere, the Surface Temperature of the Northern Hemisphere, the Sea Surface Temperature of the North Pacific and for Precipitation in the tropical Indo-Pacific. The discussion of the literature examples illustrates that the method introduced here is at least partly more sensitive than the approaches used in the literature and it allows a better quantification of the changes in the modes of variability

The Tropospheric Land-Sea Warming Contrast as the Driver of Tropical Sea Level Pressure Changes

In this article we address the causes of the large-scale tropical sea level pressure (SLP) changes during climate change. The analysis we present is based on model simulations, observed trends and the seasonal cycle. In all three cases the regional changes of tropospheric temperature (Ttropos) and SLP are strongly related to each other (considerably stronger than (sea) surface temperature and SLP). This relationship basically follows the Bjerknes Circulation Theorem, with relatively low regional SLP where we have relatively high Ttropos and vice versa. A simple physical model suggests a tropical SLP response to horizontally inhomogeneous warming in the tropical Ttropos, with a sensitivity coefficient of about -1.7 hPa/K. This relationship explains a large fraction of observed and predicted changes in the tropical SLP. It is shown that in climate change model simulations the tropospheric land-sea warming contrast is the most significant structure in the regional Ttropos changes relative to the tropical mean changes. Since the land-sea warming contrast exists in the absent of any atmospheric circulation changes it can be argued that the large-scale response of tropical SLP changes is to first order a response to the tropical land-sea warming contrast. Further, as land-sea warming contrast is mostly available moisture dependent, the models predict a stronger warming and decreasing SLP in the drier regions from South America to Africa and a weaker warming and increasing SLP over the wetter Indo-Pacific warm pool region. This suggests an increase in the potential for deep convection conditions over the Atlantic Sector and a decrease over the Indo-Pacific warm pool region in the future

Atmosphere-only GCM (ACCESS1.0) simulations with prescribed land surface temperatures

General circulation models (GCMs) are valuable tools for understanding how the global ocean-atmosphere-land surface system interacts and are routinely evaluated relative to observational data sets. Conversely, observational data sets can also be used to constrain GCMs in order to identify systematic errors in their simulated climates. One such example is to prescribe sea surface temperatures (SSTs) such that 70% of the Earth's surface temperature field is observationally constrained (known as an Atmospheric Model Intercomparison Project, AMIP, simulation). Nevertheless, in such simulations, land surface temperatures are typically allowed to vary freely, and therefore any errors that develop over the land may affect the global circulation. In this study therefore, a method for prescribing the land surface temperatures within a GCM (the Australian Community Climate and Earth System Simulator, ACCESS) is presented. Simulations with this prescribed land surface temperature model produce a mean climate state that is comparable to a simulation with freely varying land temperatures; for example, the diurnal cycle of tropical convection is maintained. The model is then developed further to incorporate a selection of "proof of concept" sensitivity experiments where the land surface temperatures are changed globally and regionally. The resulting changes to the global circulation in these sensitivity experiments are found to be consistent with other idealized model experiments described in the wider scientific literature. Finally, a list of other potential applications is described at the end of the study to highlight the usefulness of such a model to the scientific community.</p

Influences of the tropical Indian and Atlantic Oceans on the predictability of ENSO

The El Niño Southern Oscillation (ENSO) is the leading mode of climate variability and predictable on interannual time scales. Recent studies suggest that the tropical Indian and Atlantic Oceans influence the dynamics and predictability of ENSO. Here we investigate these effects in a hybrid coupled model consisting of a full complexity atmospheric general circulation model (GCM) coupled to a strongly simplified linear 2-dimensional ENSO recharge oscillator ocean model. We find that the tropical Indian and Atlantic Oceans have distinct effects on the dynamics and predictability. The decoupling of the tropical Indian Ocean has a strong impact onto ENSO dynamics, but the initial conditions of it have only a small impact on the ENSO predictability. In contrast, initial conditions of the tropical Atlantic have a stronger impact on the predictability of ENSO, but the decoupling of the tropical Atlantic has almost no effect on the ENSO dynamics

Eastward shift of the Walker Circulation under global warming and its relationship to ENSO variability

© 2014, Springer-Verlag Berlin Heidelberg.This study investigates the global warming response of the Walker Circulation and the other zonal circulation cells (represented by the zonal stream function), in CMIP3 and CMIP5 climate models. The changes in the mean state are presented as well as the changes in the modes of variability. The mean zonal circulation weakens in the multi model ensembles nearly everywhere along the equator under both the RCP4.5 and SRES A1B scenarios. Over the Pacific the Walker Circulation also shows a significant eastward shift. These changes in the mean circulation are very similar to the leading mode of interannual variability in the tropical zonal circulation cells, which is dominated by El Niño Southern Oscillation variability. During an El Niño event the circulation weakens and the rising branch over the Maritime Continent shifts to the east in comparison to neutral conditions (vice versa for a La Niña event). Two-thirds of the global warming forced trend of the Walker Circulation can be explained by a long-term trend in this interannual variability pattern, i.e. a shift towards more El Niño-like conditions in the multi-model mean under global warming. Further, interannual variability in the zonal circulation exhibits an asymmetry between El Niño and La Niña events. El Niño anomalies are located more to the east compared with La Niña anomalies. Consistent with this asymmetry we find a shift to the east of the dominant mode of variability of zonal stream function under global warming. All these results vary among the individual models, but the multi model ensembles of CMIP3 and CMIP5 show in nearly all aspects very similar results, which underline the robustness of these results. The observed data (ERA Interim reanalysis) from 1979 to 2012 shows a westward shift and strengthening of the Walker Circulation. This is opposite to what the results in the CMIP models reveal. However, 75 % of the trend of the Walker Circulation can again be explained by a shift of the dominant mode of variability, but here towards more La Niña-like conditions. Thus in both climate change projections and observations the long-term trends of the Walker Circulation seem to follow to a large part the pre-existing dominant mode of internal variability

The Annual Peak in the SST Anomaly Spectrum

The manner in which monthly mean sea surface temperature anomalies (SSTAs) show enhanced variance at the annual period in the extratropics (an annual peak in the variance spectrum) is illustrated by observations and model simulations. A mechanism, related to the reemergence of winter SST anomalies, is proposed to explain the annual peak in SST spectrum. The idea is supported by the analysis of a hierarchy of models, including Intergovernmental Panel on Climate Change model simulations. The results of the model experiments further suggest that the annual peak is either weak or absent if decadal SST variability is forced by local air–sea interaction. However, if ocean subsurface temperature variability forces decadal SST variability, the annual peak is much stronger. Strong annual peaks may therefore be seen as an indication of ocean-forced decadal SST variability in the extratropics

Impacts of the tropical Indian and Atlantic Oceans on ENSO

The impacts of the tropical Indian and Atlantic Oceans on ENSO are studied using a series of 500 years long GCM simulations, in which the tropical Indian and/or Atlantic Ocean SSTs are fixed. The results indicate that the tropical Indian and/or Atlantic Oceans SST anomalies substantially influence the coupling over the equatorial Pacific. In the absence of SST variability in the tropical Indian and/or Atlantic Ocean, the main ENSO period is shifted by almost one year. The total SST variance in the equatorial Pacific region is reduced if either Indian or Atlantic Ocean variability is present. At the same time the atmospheric ENSO teleconnections are damped more strongly than the SST. The results can be understood in the context of the recharge oscillator model. However, it is difficult to verify the feedback of the Indian and/or Atlantic Oceans onto ENSO only with statistical analyses of the coupled model control integration or observations

The Ocean's Role in Continental Climate Variability and Change

A characteristic feature of global warming is the land-sea contrast, with stronger warming over land than over oceans. Recent studies find that this land-sea contrast also exists in equilibrium global change scenarios, and it is caused by differences in the availability of surface moisture over land and oceans. In this study it is illustrated that this land-sea contrast exists also on interannual time scales and that the ocean-land interaction is strongly asymmetric. The land surface temperature is more sensitive to the oceans than the oceans are to the land surface temperature, which is related to the processes causing the land-sea contrast in global warming scenarios. It suggests that the ocean's natural variability and change is leading to variability and change with enhanced magnitudes over the continents, causing much of the longer-time-scale (decadal) global-scale continental climate variability. Model simulations illustrate that continental warming due to anthropogenic forcing (e. g., the warming at the end of the last century or future climate change scenarios) is mostly (80%-90%) indirectly forced by the contemporaneous ocean warming, not directly by local radiative forcing