Bulk Connectivity of Global SST and Land Precipitation Variations

The land climate predictability at seasonal and interannual time scales is largely due to the influence of the ocean. The connections between global sea surface temperature anomaly (SSTA) and precipitation anomaly over land as a whole are assessed using observations and Atmospheric Model Intercomparison Project (AMIP) simulations for 1957-2018 in this work. With a novel bulk connectivity matrix, the regions of SSTA having the most significant connections with global land precipitation anomaly are identified and the seasonal evolution is evaluated. The similarities and differences between the observations and AMIP simulations are examined. In both observations and AMIP simulations, SSTA in the tropical central and eastern Pacific connects strongly with the global land precipitation anomaly. Compared with that in the tropical Pacific, the connections with SSTA along the equatorial Indian and Atlantic Oceans are weaker. However, the seasonal evolution of the connection shows distinguished patterns between the observations and the AMIP simulations with the strongest (weakest) connections in October (June) in the observations, in March and October (June) in a single-member of the AMIP simulation, and in February (June) in the 17-member ensemble mean of the AMIP simulations. The ensemble averaging enhances the strength of the connectivity and improves its seasonality. The results of the bulk connectivity matrix in this work can serve as a benchmark to evaluate the connection of SSTA with global land precipitation variation in climate models.

Monsoon Regimes in the CCSM3

Simulations of regional monsoon regimes, including the Indian, Australian, West African, South American, and North American monsoons, are described for the T85 version of the Community Climate System Model version 3 (CCSM3) and compared to observations and Atmospheric Model Intercomparison Project (AMIP)-type SST-forced simulations with the Community Atmospheric Model version 3 (CAM3) at T42 and T85. There are notable improvements in the regional aspects of the precipitation simulations in going to the higher-resolution T85 compared to T42 where topography is important (e.g., Ethiopian Highlands, South American Andes, and Tibetan Plateau). For the T85 coupled version of CCSM3, systematic SST errors are associated with regional precipitation errors in the monsoon regimes of South America and West Africa, though some aspects of the monsoon simulations, particularly in Asia, improve in the coupled model compared to the SST-forced simulations. There is very little realistic intraseasonal monsoon variability in the CCSM3 consistent with earlier versions of the model. Teleconnections to the tropical Pacific are well simulated for the South Asian monsoon.

Snow Mass over North America: Observations and Results from the Second Phase of the Atmospheric Model Intercomparison Project

Eighteen global atmospheric general circulation models (AGCMs) participating in the second phase of the Atmospheric Model Intercomparison Project (AMIP-2) are evaluated for their ability to simulate the observed spatial and temporal variability in snow mass, or water equivalent (SWE), over North America during the AMIP-2 period (1979–95). The evaluation is based on a new gridded SWE dataset developed from objective analysis of daily snow depth observations from Canada and the United States with snow density estimated from a simple snowpack model. Most AMIP-2 models simulate the seasonal timing and the relative spatial patterns of continental-scale SWE fairly well. However, there is a tendency to overestimate the rate of ablation during spring, and significant between-model variability is found in every aspect of the simulations, and at every spatial scale analyzed. For example, on the continental scale, the peak monthly SWE integrated over the North American continent in AMIP-2 models varies between ±50% of the observed value of ∼1500 km3. The volume of water in the snowpack, and the magnitudes of model errors, are significant in comparison to major fluxes in the continental water balance. It also appears that the median result from the suite of models tends to do a better job of estimating climatological mean features than any individual model. Year-to-year variations in large-scale SWE are only weakly correlated to observed variations, indicating that sea surface temperatures (specified from observations as boundary conditions) do not drive interannual variations of SWE in these models. These results have implications for simulations of the large-scale hydrologic cycle and for climate change impact assessments.

Evaluation of Soil Moisture in the NCEP–NCAR and NCEP–DOE Global Reanalyses

This study compares soil moisture analyses from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) global reanalysis (R-1) and the later NCEP– Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP) global reanalysis (R-2). The R-1 soil moisture is strongly controlled by nudging it to a prescribed climatology, whereas the R-2 soil moisture is adjusted according to differences between model-generated and observed precipitation. While mean soil moisture fields from R-1 and R-2 show many geographic similarities, there are some major differences. This study uses in situ observations from the Global Soil Moisture Data Bank to evaluate the two global reanalysis products. In general, R-2 does a better job of simulating interannual variations, the mean seasonal cycle, and the persistence of soil moisture, when compared to observations. However, the R-2 reanalysis does not necessarily represent observed soil moisture characteristics well in all aspects. Sometimes R-1 provides a better soil moisture analysis on monthly time scales, which is likely a consequence of the deficiencies in the R-2 surface water balance.

The Role of Boundary Conditions in AMIP-2 Simulations of the NAO

The simulated North Atlantic Oscillation (NAO) teleconnection patterns and their interannual variability are evaluated from a suite of atmospheric models participating in the second phase of the Atmospheric Model Intercomparison Project (AMIP-2). In general the models simulate the observed spatial pattern well, although there are important differences among models. The NAO response to interannual variations in sea surface temperature (SST) and snow-cover boundary forcings are also evaluated. The simulated NAO indices are not correlated with the observed NAO index, despite being forced with observed SSTs, indicating that SSTs are not driving NAO variability in the models. Similarly, although a number of studies have identified a link between Eurasian snow extent and the phase of the NAO, no such link is apparent in the AMIP-2 results. It appears that, within the framework of the AMIP-2 experiments, the NAO is an internal mode of atmospheric variability and that impacts of SSTs and Eurasian snow cover on the phase of the NAO are not discernable. However, these conclusions do not necessarily apply to decadal-scale and longer variability or to coupled atmosphere–ocean models.