Thomas M. Smith

Variations in annual global precipitation (1979–2004), based on the Global Precipitation Climatology Project 2.5° analysis

[1]xa0The Global Precipitation Climatology Project (GPCP) has produced a combined satellite and in situ global precipitation estimate, beginning 1979. The annual average GPCP estimates are here analyzed over 1979–2004 to evaluate the large-scale variability over the period. Data inhomogeneities are evaluated and found to not be responsible for the major variations, including systematic changes over the period. Most variations are associated with El Nino/Southern Oscillation (ENSO) episodes. There are also tropical trend-like changes over the period, correlated with interdecadal warming of the tropical SSTs and uncorrelated with ENSO. Trends have spatial variations with both positive and negative values, with a global-average near zero.

New surface temperature analyses for climate monitoring

[1]xa0Global surface temperature is a critical measure of climate variation. Here the averages of a new surface-temperature analysis are compared to an estimate of the global average which has been used for monitoring surface-temperature variations at NOAAs National Climatic Data Center (NCDC) since 1998. As a replacement to the existing method, this new analysis uses improved methods that provide error estimates as well as the ability to perform analyses on finer spatial scales. Comparisons show only minor global-average differences, and the two estimates indicate essentially the same trend over the historical record, beginning in 1880. The two are most similar after about 1970, a period with a large change in the global-average temperature. The uncertainty estimates computed here account for changes in sampling and for systematic bias uncertainties. The means of the different analyses generally fall within the uncertainty estimates. The uncertainty computed here indicates that anomalies in the 19th century may not be significant, but the 20th century trends are significant.

A new merged analysis of precipitation utilizing satellite and reanalysis data

[1]xa0Many merged multi-source global analyses of precipitation exist, including the Global Precipitation Climatology Project (GPCP) analysis and the CPC Merged Analysis of Precipitation. The multi-source nature of these data sets allows them to use the most accurate type of inputs available to produce the best estimate of precipitation for any given place and time. However, studies have shown that the oceanic satellite estimates used in these data sets are less accurate at high latitudes when compared to reanalysis data. This study describes the Multi-Source Analysis of Precipitation (MSAP), a new 2.5° gridded global analysis of precipitation from 1987 to 2002 using Optimum Interpolation (OI) based on the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and the forecast precipitation from the ERA-40 reanalysis. The main goal of this new analysis is to produce a spatially consistent estimate using the same set of inputs over all regions and times rather than to have the lowest mean squared error. An advantage of the OI methodology is that it optimally merges the inputs based on pre-defined weights and errors associated with the analysis that are directly estimated from the technique. Validation against other gridded data sets as well as tropical ocean and high-latitude land gauges show that MSAP performs particularly well at high latitudes when compared to the satellite-only part of GPCP. However, it contains negative biases in parts of the Northern Hemisphere because of the ERA-40 data and large positive biases over tropical land areas due to issues with the SSM/I estimates. In the future, this new approach can be applied using precipitation estimates from the next generation reanalysis systems such as the JRA-25, NASAs MERRA, and the ERA Interim reanalysis.

Reconstruction of near‐global annual precipitation using correlations with sea surface temperature and sea level pressure

[1]xa0An indirect precipitation analysis method is described which allows analysis of large spatial-scale and multidecadal variations over land and oceans beginning 1900. The method uses covariance between precipitation and analyses of sea level pressure (SLP) and sea surface temperature (SST). Both SLP and SST analyses can be produced using in situ data for the 20th century. Here a canonical correlation analysis is developed to specify annual precipitation anomalies from annual anomalies of SLP and SST on a 5° spatial grid. Covariance relationships are computed using 26 years of satellite-based precipitation data beginning 1979 and are used to analyze annual average precipitation anomalies for the full period. This indirect analysis indicates global variations consistent with the satellite-based analysis for the recent period. Cross-validation testing shows most skill in the tropics where variations are largest, with decreasing skill at higher latitudes, and large-scale averages have much more skill than at individual locations. For the full period over oceans the analysis indicates increasing precipitation with increasing temperature over the 20th century. That oceanic change is correlated with the change from climate models, but the analysis change is more than twice as strong as the change indicated by the models. Over land the analysis is consistent with gauge observations over the 20th century, which are independent observations before 1979. This study shows that indirect precipitation analyses can show many climate-scale variations that cannot be resolved in studies based on direct analysis of precipitation data.

Historical reconstruction of monthly oceanic precipitation (1900–2006)

[1]xa0An oceanic precipitation reconstruction is developed and evaluated for periods extending back to as early as 1900. Large-scale monthly oceanic precipitation is reconstructed using the available network of historical gauge data fit to a set of large-scale covariance spatial modes based on modern analysis. The modern analysis is based on satellite microwave estimates merged with atmospheric reanalysis estimates. For 1900–2006, a monthly and 5° spatial product is developed for reconstructing climate-scale (large-scale spatial and inter-seasonal and longer timescales) variation in pre-satellite periods. Advantages of this study include the use of a homogeneous satellite analysis for computing reconstruction statistics and improved tuning of the reconstruction methods. Cross-validation testing is used to show how much historical variance can be reconstructed with the available sampling. In addition, reconstructions are performed using several different gauge data sets to evaluate differences due to the choice of gauge data. Testing shows that that these improved methods produce reconstructions with useful skill in the tropics and Northern Hemisphere midlatitudes. In the Southern Hemisphere midlatitudes and in polar latitudes in both hemispheres, sampling is too sparse to yield much skill. The reconstruction is able to resolve large-scale modes of variation useful for some diagnostic studies of the 20th century. Reconstruction efforts will continue as improved satellite and atmospheric reanalyses become available.

Detection of recent regional sea surface temperature warming in the Caribbean and surrounding region

We show a sea surface temperature (SST) warming trend for the Caribbean and surrounding region over 1982–2012. Using an optimum interpolated SST product, a 30u2009year climatological analysis was generated to observe annual, monthly, and seasonal trends. Results show that SSTs are increasing annually for the region. For the two Caribbean rainy seasons, the Early Rainfall Season (ERS) and the Late Rainfall Season (LRS), estimated trends at 0.0161°Cu2009yr−1 and 0.0209°Cu2009yr−1 were observed, with high statistical significance. Subregional analysis revealed that warming is greatest in the Gulf of Mexico and north of South America during the ERS and LRS. Additionally, LRS averages for 1998–2012 reflect an increase in magnitude and intensity of the Atlantic Warm Pool (AWP) since the 1983–1997 period reflected in the AWP Area Index. Extreme increases/decreases in the time series show potential correlation with El Nino and the Southern Oscillation.

Modes of multi-decadal oceanic precipitation variations from a reconstruction and AR4 model output for the 20th century

[1]xa0Monthly oceanic precipitation variations are available from satellite observations beginning 1979 and from models for earlier periods, and both the observations and models indicate increasing global-average precipitation with warming global temperatures. Recently the authors developed an indirect data-based reconstruction of precipitation beginning 1900, which is used here to analyze near-global average multi-decadal oceanic variations through the 20th century. We compare this new reconstruction to oceanic coupled-model precipitation from AR4 models beginning 1900, and to GPCP precipitation beginning 1979. Both the reconstruction and AR4 models indicate increasing precipitation over the 20th century. The reconstruction increase is stronger than the AR4 increase largely due to a climate shift in the 1970s that is resolved by the reconstruction but absent in the AR4 ensemble. The reconstruction climate shift has an ENSO-like spatial pattern and in the reconstruction the shift is associated with an ENSO mode, consistent with work done by others. The influence of this mode indicates the need for coupled models to accurately simulate the tropical Pacific in order to resolve that regions influence on multi-decadal variations.

Developing a Historical Precipitation Record

Knowing historical precipitation is important for climate monitoring and for evaluating coupled climate models designed to simulate changes in precipitation associated with climate change. Over land gauge-based analyses are sufficient to determine large-scale variations over the twentieth century. Over oceans satellite-based analyses can be used beginning 1979. However, there are few direct or remote sensing observations of oceanic precipitation variations before 1979. For the pre-satellite time, it is possible to use reconstructions based on the available data to analyze some oceanic precipitation variations. Evaluations of the available data and methods have shown that large-scale variations in twentieth-century oceanic precipitation may be reconstructed. Reconstructions based on spatial covariance and historical gauge data represent seasonal to interannual variations. Reconstructions based on correlations with sea-surface temperature (SST) and sea-level pressure (SLP) represent multi-decadal variations. Combining these two types of reconstructions yields a merged reconstruction with the best features of both.