The IRI Seasonal Climate Prediction System and the 1997/98 El Niño Event

The International Research Institute for Climate Prediction (IRI) was formed in late 1996 with the aim of fostering the improvement, production, and use of global forecasts of seasonal to interannual climate variability for the explicit benefit of society. The development of the 1997/98 El Niño provided an ideal impetus to the IRI Experimental Forecast Division (IRI EFD) to generate seasonal climate forecasts on an operational basis. In the production of these forecasts an extensive suite of forecasting tools has been developed, and these are described in this paper. An argument is made for the need for a multimodel ensemble approach and for extensive validation of each model's ability to simulate interannual climate variability accurately. The need for global sea surface temperature forecasts is demonstrated. Forecasts of precipitation and air temperature are presented in the form of "net assessments," following the format adopted by the regional consensus forums. During the 1997/98 El Niño,the skill of the net assessments was greater than chance, except over Europe, and in most cases was an improvement over a forecast of persistence of the latest month's climate anomaly

Challenges in quantifying changes in the global water cycle

Human influences have likely already impacted the large-scale water cycle but natural variability and observational uncertainty are substantial. It is essential to maintain and improve observational capabilities to better characterize changes. Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time-series over land but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols, and due to large climate variability presently limits confidence in attribution of observed changes

Overview of the Large-Scale Biosphere–Atmosphere Experiment in Amazonia Data Model Intercomparison Project (LBA-DMIP)

A fundamental question connecting terrestrial ecology and global climate change is the sensitivity of key terrestrial biomes to climatic variability and change. The Amazon region is such a key biome: it contains unparalleled biological diversity, a globally significant store of organic carbon, and it is a potent engine driving global cycles of water and energy. The importance of understanding how land surface dynamics of the Amazon region respond to climatic variability and change is widely appreciated, but despite significant recent advances, large gaps in our understanding remain. Understanding of energy and carbon exchange between terrestrial ecosystems and the atmosphere can be improved through direct observations and experiments, as well as through modeling activities. Land surface/ecosystem models have become important tools for extrapolating local observations and understanding to much larger terrestrial regions. They are also valuable tools to test hypothesis on ecosystem functioning. Funded by NASA under the auspices of the LBA (the Large-Scale Biosphere–Atmosphere Experiment in Amazonia), the LBA Data Model Intercomparison Project (LBA-DMIP) uses a comprehensive data set from an observational network of flux towers across the Amazon, and an ecosystem modeling community engaged in ongoing studies using a suite of different land surface and terrestrial ecosystem models to understand Amazon forest function. Here an overview of this project is presented accompanied by a description of the measurement sites, data, models and protocol

Phenotypic Signatures Arising from Unbalanced Bacterial Growth

Fluctuations in the growth rate of a bacterial culture during unbalanced growth are generally considered undesirable in quantitative studies of bacterial physiology. Under well-controlled experimental conditions, however, these fluctuations are not random but instead reflect the interplay between intra-cellular networks underlying bacterial growth and the growth environment. Therefore, these fluctuations could be considered quantitative phenotypes of the bacteria under a specific growth condition. Here, we present a method to identify “phenotypic signatures” by time-frequency analysis of unbalanced growth curves measured with high temporal resolution. The signatures are then applied to differentiate amongst different bacterial strains or the same strain under different growth conditions, and to identify the essential architecture of the gene network underlying the observed growth dynamics. Our method has implications for both basic understanding of bacterial physiology and for the classification of bacterial strains

Diagnosis of ENSO-related precipitation changes during the twentieth and twenty-first centuries using reanalyses and two multi-model clusters

Changes in El Nino-Southern Oscillation (ENSO)-related precipitation anomalies during the twentieth century are poorly known. The ENSO-related precipitation anomalies over the tropical Pacific Ocean are projected to shift eastward during the twenty-first century. However, intermodel diversity in simulating and projecting these changes has not been fully studied. Understanding such diversity is vital for providing robust projections of future changes in the ENSO-related precipitation. Here, we use an approach that can cleanly separate ENSO signals from long-term variations to study multi-decadal and centennial changes of ENSO-related precipitation anomalies during the twentieth and twenty-first centuries in two precipitation reanalyses and two groups of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models. The two precipitation reanalyses disagree in the multi-decadal changes during the twentieth century: precipitation anomalies in the twentieth century global circulation reanalysis indicates zonal shifts of the positive and negative ENSO-related precipitation anomalies, whereas reconstructed precipitation derived from observations suggests an intensification of the anomalies. Most members of the ensemble of 20 CMIP5 models project significant strengthening or zonal shift in ENSO-related precipitation during the twenty-first century, which is almost entirely influenced by changes in the ENSO-related circulation from a moisture budget analysis. The 'good-ENSO' group with stronger ENSO atmospheric feedbacks projects an intensification of ENSO-related precipitation anomalies over the eastern-central equatorial Pacific, as a result of strengthened deep convection anomalies in the middle to upper troposphere above. This increased variability of convection is driven by a warmer SST mean state of the central-eastern Pacific Ocean. The underperforming group that simulates colder equatorial SSTs, weaker atmospheric feedbacks, and more westward extended ENSO-related precipitation anomalies, exhibits both strong intensification and eastward shift of the entire ENSO-related air-sea interactive system and the related precipitation over the western-central Pacific during the twenty-first century, in correspondence with a mean-state SST warming in the entire equatorial Pacific. Our clustered results suggest that the changes of ENSO-related precipitation are closely tied with the historical simulation of structure of the ENSO-related air-sea system and the projection of changes in the mean-state SST under global warming

Interannual variability in precipitation over the Southern Hemisphere: What have we learned since 1985?

Precipitation is a critical element of the climate of the Southern Hemisphere (SH), and observations of its mean annual cycle and interannual variability are crucial to understanding SH climate variability. Twenty-two years ago, at the time of the first Conference on Southern Hemisphere Meteorology in Sao Jose dos Campos, Brazil, our knowledge of SH precipitation over land was based on rain gauge observations, yielding climatologies with excellent detail but with much less information on year-to-year variability. Over the Southern Ocean (SO) the situation was even less satisfactory, as our knowledge was limited to climatologies based on a variety of limited information, including ship observations of present weather and island rain gauges; no time series of precipitation analyses existed. Linking land and oceanic precipitation variability was essentially impossible aside from some limited information that was available from convective indices based on infrared satellite observations for the tropics and subtropics. At the present, we have global time series of analyses of monthly and pentad precipitation from the Global Precipitation Climatology Project (GPCP) and CPC Merged Analysis of Precipitation (CMAP), both based on the combination of information from passive microwave and infrared sensors on both polar orbiting and geostationary satellites. We also have powerful new observations, including those from the Tropical Rainfall Measuring Mission, as well as new algorithms capable of deriving high resolution precipitation analyses for much of the globe. These multiple data sets have proven useful for a wide variety of climate studies, from the description of intraseasonal and interannual variability to the validation of global weather and climate forecast models. However, a number of major concerns exist with these data sets. The global analyses of the GPCP and CMAP have significant inadequacies, including inhomogeneities in input data and methodology, temporal and spatial artifacts, the inability to clearly define decadal and longer variability and a failure to adequately resolve the global water and energy budgets. To a substantial extent, these issues arise from gaps and changes in the global observing system, such as the advent of passive microwave observations in mid-1987 and the continued development of such instruments, the availability of the TRMM radar since late 1997, and the evolution of the global geostationary satellite network since 1980. In this paper, we will describe the mean annual cycle and interannual variability in SO precipitation as depicted in the GPCP and CMAP, and attempt to identify the robust findings as well as the ambiguities and shortcomings. We will examine the finer scale detail in regions of interest using the newer finer resolution datasets such as the TRMM RT and CMORPH, and will describe the initial results of the Pilot Evaluation of High Resolution Precipitation Products, an international collaboration involving producers and users of precipitation datasets using satellite and in situ observations.Pages: 1465-146

B.: Impact of the North Atlantic Oscillation on the middle eastern climate and streamflow

Abstract. Interannual to decadal variations in Middle Eastern temperature, precipitation and streamflow reflect the far-field influence of the North Atlantic Oscillation (NAO), a dominant mode of Atlantic sector climate variability. Using a new sea surface temperature (SST) based index of the NAO and available streamflow data from five Middle Eastern rivers, we show that the first principal component of December through March streamflow variability reflects changes in the NAO. However, Middle East rivers have two primary flooding periods. The first is rainfall-driven runoff from December through March, regulated on interannual to decadal timescales by the NAO as reflected in local precipitation and temperature. The second period, from April through June, reflects spring snowmelt and contributes in excess of 50% of annual runoff. This period, known locally as the khamsin, displays no significant NAO connections and a less direct relationship with local climatic factors, suggesting that streamflow variability during this period reflects land-cover change, possibly related to agriculture and hydropower generation, and snowmelt

Satellite Rainfall Estimates Over South America - Possible Applicability to the Water Management of Large Watersheds1

This work analyzes high-resolution precipitation data from satellite-derived rainfall estimates over South America, especially over the Amazon Basin. The goal is to examine whether satellite-derived precipitation estimates can be used in hydrology and in the management of larger watersheds of South America. High spatial-temporal resolution precipitation estimates obtained with the CMORPH method serve this purpose while providing an additional hydrometeorological perspective on the convective regime over South America and its predictability. CMORPH rainfall estimates at 8-km spatial resolution for 2003 and 2004 were compared with available rain gauge measurements at daily, monthly, and yearly accumulation time scales. The results show the correlation between satellite-derived and gauge-measured precipitation increases with accumulation period from daily to monthly, especially during the rainy season. Time-longitude diagrams of CMORPH hourly rainfall show the genesis, strength, longevity, and phase speed of convective systems. Hourly rainfall analyses indicate that convection over the Amazon region is often more organized than previously thought, thus inferring that basin scale predictions of rainfall for hydrological and water management purposes have the potential to become more skillful. Flow estimates based on CMORPH and the rain gauge network are compared to long-term observed average flow. The results suggest this satellite-based rainfall estimation technique has considerable utility. Other statistics for monthly accumulations also suggest CMORPH can be an important source of rainfall information at smaller spatial scales where in situ observations are lacking.National Council for Scientific and Technological Development (CNPq)[301724/2008-3