January-february Tropospheric Climate for the Northern Hemisphere and the 11-year Solar Cycle, the QBO and the Southern Oscillation

Examined here is a recently discovered association between the 11-year solar cycle and the atmosphere that is most easily detectable when the two phases of the Quasi-biennial Oscillation (QBO) are considered individually rather than pooled. The influence of the Southern Oscillation (SO) for either of the two QBO phases is then combined with that of the solar cycle in the form of two-predictor multiple regression. The strong and well-defined relationship between the 11-year 10.7 cm solar flux cycle and the lower troposphere Northern Hemisphere January-February climate for QBO phase-stratified samples (van Loon and Labitzke 1988, Barnston and Livezey 1989) failed for the west QBO phase in 1989. Here, the opposing 1989 event is explained, at least in part, on the basis of the phase of the SO (the cold tropical Pacific SST event of 1988 to 1989). It is demonstrated that both the SO and the solar flux have moderate and quasi-independent correlations with the climate over certain regions, and where there is strong overlap they can work either in harmony or in opposition. In 1989 in North America the influences of the SO and the flux conflicted to an unprecedented extent, and the SO was the controlling influence in most regions of the continent (western Canada being one exception). The 1989 event draws attention to the smallness of the QBO phase-stratified samples and the still more serious holes in the two-dimensional sample space of flux and SO when both factors are viewed as predictors within one QBO phase

Description and Skill Evaluation of Experimental Dynamical Seasonal Forecasts of Tropical Cyclone Activity at IRI

The International Research Institute for Climate and Society has been issuing experimental seasonal tropical cyclone activity forecasts for several ocean basins since early 2003. In this paper we describe the method used to obtain these forecasts, and evaluate their performance. The forecasts are based on tropical cyclone-like features detected and tracked in a low-resolution climate model, namely ECHAM4.5. The simulation skill of the model using historical observed sea surface temperatures (SSTs) over several decades, as well as with SST anomalies persisted from the month ending at the forecast start time, is discussed. These simulation skills are compared with skills of purely statistically based hindcasts using as predictors observed SSTs preceding the forecast start time. For the recent 6-year period during which real-time forecasts have been made, the skill of the raw model output is compared with that of the subjectively modified probabilistic forecasts actually issued. Despite variations from one basin to another, the hindcast skills of the dynamical and statistical forecast approaches are found, overall, to be approximately equivalent. The dynamical forecasts require statistical post-prossessing (calibration) to be competitive with, and in some circumstances superior to, the statistical models. Hence, during the recent period of real-time forecasts, the subjective forecasts are found to have resulted in probabilistic skill better than that of the raw model output, primarily because of the forecasters' elimination of the systematic bias of "overconfidence" in the model's forecasts. Prospects for the future improvement of dynamical tropical cyclone prediction are considered

Evaluation of IRI's seasonal climate forecasts for the extreme 15% tails

This paper evaluates the quality of real-time seasonal probabilistic forecasts of the extreme 15% tails of the climatological distribution of temperature and precipitation issued by the International Research Institute for Climate and Society (IRI) from 1998 through 2009. IRI’s forecasts have been based largely on a two-tiered multimodel dynamical prediction system. Forecasts of the 15% extremes have been consistent with the corresponding probabilistic forecasts for the standard tercile-based categories; however, nonclimatological forecasts for the extremes have been issued sparingly. Results indicate positive skill in terms of resolution and discrimination for the extremes forecasts, particularly in the tropics. Additionally, with the exception of some overconfidence for extreme above-normal precipitation and a strong cool bias for temperature, reliability analyses suggest generally good calibration. Skills for temperature are generally higher than those for precipitation, due both to correct forecasts of increased probabilities of extremely high (above the upper 15th percentile) temperatures associated with warming trends, and to better discrimination of interannual variability. However, above-normal temperature extremes were substantially underforecast, as noted also for the IRI’s tercile forecasts

The role of ENSO in understanding changes in Colombia's annual malaria burden by region, 1960–2006

Malaria remains a serious problem in Colombia. The number of malaria cases is governed by multiple climatic and non-climatic factors. Malaria control policies, and climate controls such as rainfall and temperature variations associated with the El Niño/Southern Oscillation (ENSO), have been associated with malaria case numbers. Using historical climate data and annual malaria case number data from 1960 to 2006, statistical models are developed to isolate the effects of climate in each of Colombia's five contrasting geographical regions. Because year to year climate variability associated with ENSO causes interannual variability in malaria case numbers, while changes in population and institutional control policy result in more gradual trends, the chosen predictors in the models are annual indices of the ENSO state (sea surface temperature [SST] in the tropical Pacific Ocean) and time reference indices keyed to two major malaria trends during the study period. Two models were used: a Poisson and a Negative Binomial regression model. Two ENSO indices, two time reference indices, and one dummy variable are chosen as candidate predictors. The analysis was conducted using the five geographical regions to match the similar aggregation used by the National Institute of Health for its official reports. The Negative Binomial regression model is found better suited to the malaria cases in Colombia. Both the trend variables and the ENSO measures are significant predictors of malaria case numbers in Colombia as a whole, and in two of the five regions. A one degree Celsius change in SST (indicating a weak to moderate ENSO event) is seen to translate to an approximate 20% increase in malaria cases, holding other variables constant. Regional differentiation in the role of ENSO in understanding changes in Colombia's annual malaria burden during 1960–2006 was found, constituting a new approach to use ENSO as a significant predictor of the malaria cases in Colombia. These results naturally point to additional needed work: (1) refining the regional and seasonal dependence of climate on the ENSO state, and of malaria on the climate variables; (2) incorporating ENSO-related climate variability into dynamic malaria models

Properties of Tropical Cyclones in Atmospheric General Circulation Models

The properties of tropical cyclones in three atmospheric general circulation models (AGCMs) with low-resolution are discussed. The models are analysed for a period of 40 years. Characteristics of the tropical cyclones in the models are analysed and compared with those of observations, such as genesis position, number of cyclones, accumulated cyclone activity, number of storm days, tracks, and others. The three AGCMs have different levels of skill in simulating the different aspects of tropical cyclone activity in different regions. Some of the weak and strong features in simulating tropical cyclone activity variables are common for the three models, others are unique for each model and basin. The relation between model tropical cyclones and ENSO is analyzed in a paper currently in preparation

Emerging La Niña conditions in the equatorial Pacific : notes for the health community

This report provides information to assist monitoring of vulnerable communities and provide time sensitive information for interventions to reduce negative health impacts. It is prudent for health decision makers to follow the situation for any developments and monitor climate/weather forecasts as part of an early warning-early action approach. Resources and recommendations for monitoring the situation are presented

Estimation of Seasonal Precipitation Tercile-Based Categorical Probabilities from Ensembles

Ensemble simulations and forecasts provide probabilistic information about the inherently uncertain climate system. Counting the number of ensemble members in a category is a simple nonparametric method of using an ensemble to assign categorical probabilities. Parametric methods of assigning quantile-based categorical probabilities include distribution fitting and generalized linear regression. Here the accuracy of counting and parametric estimates of tercile category probabilities is compared. The methods are first compared in an idealized setting where analytical results show how ensemble size and level of predictability control the accuracy of both methods. The authors also show how categorical probability estimate errors degrade the rank probability skill score. The analytical results provide a good description of the behavior of the methods applied to seasonal precipitation from a 53-yr, 79-member ensemble of general circulation model simulations. Parametric estimates of seasonal precipitation tercile category probabilities are generally more accurate than the counting estimate. In addition to determining the relative accuracies of the different methods, the analysis quantifies the relative importance of the ensemble mean and variance in determining tercile probabilities. Ensemble variance is shown to be a weak factor in determining seasonal precipitation probabilities, meaning that differences between the tercile probabilities and the equal-odds probabilities are due mainly to shifts of the forecast mean away from its climatological value

A statistical assessment of tropical cyclone activity in atmospheric general circulation models

The properties of tropical cyclones in three low-resolution atmospheric general circulation models (AGCMs) in seven ocean basins are discussed. The models are forced by prescribed, observed sea surface temperatures over a period of 40 yr, and their simulations of tropical cyclone activity are compared with observations. The model cyclone characteristics considered include genesis position, number of cyclones per year, seasonality, accumulated cyclone energy, track locations, and number of storm days. Correlations between model and observed interannual variations of these characteristics are evaluated. The models are found able to reproduce the basic features of observed tropical cyclone behavior such as seasonality, general location and interannual variability, but with identifiable biases. A bias correction is applied to the tropical cyclone variables of the three models. The three AGCMs have different levels of realism in simulating different aspects of tropical cyclone activity in different ocean basins. Some strengths and weaknesses in simulating certain tropical cyclone activity variables are common to the three models, while others are unique to each model and/or basin. Although the overall skill of the models in reproducing observed interannual variability of tropical cyclone variables has not surpassed or often even equalled that of statistical models, there exists potential for higher future skills using improved versions of dynamical approaches

Climate information, outlooks, and understanding–where does the IRI stand?

The International Research Institute for Climate and Society (IRI) began providing user-oriented climate information, including outlooks, in the late 1990s. Its climate products are intended to meet the needs of decision makers in various sectors of society such as agriculture, water management, health, disaster management, energy, education and others. They try to link the current state of the science in climate diagnostics and prediction to the dynamically evolving practical needs of users worldwide. Because most users are not climate scientists, the manner in which the information is provided is of paramount importance in order for it to be understandable and actionable. Non-technical language that preserves essential content is required, as well as graphics that are intuitive and largely self-explanatory. The climate information products themselves must be in demand by users, rather than ones that the producers believe would be best. These requirements are consistent with IRI’s mission of improving human welfare, particularly in developing countries where decision makers may not initially know what climate information they need, and how best to use it. This lack of initial understanding requires back-and-forth communication between the producers and users to initiate and sustain uptake and beneficial use of the information. Backed by its climate prediction research, the IRI’s climate information products span time-scales of days to decades. Experience on the statistics of daily weather behavior within seasons has been gleaned, as has the benefits of statistical and dynamical spatial downscaling of predictions. By providing views in a progressive sequence of temporal scales, IRI’s products help demonstrate that preparation for interannual climate variability may be the best preparation for decadal variability and trends related to climate change

Experimental dynamical seasonal forecasts of tropical cyclone activity at IRI

The International Research Institute for Climate and Society (IRI) has been issuing experimental seasonal tropical cyclone activity forecasts for several ocean basins since early 2003. In this paper the method used to obtain these forecasts is described and the forecast performance is evaluated. The forecasts are based on tropical cyclone–like features detected and tracked in a low-resolution climate model, namely ECHAM4.5. The simulation skill of the model using historical observed sea surface temperatures (SSTs) over several decades, as well as with SST anomalies persisted from the previous month’s observations, is discussed. These simulation skills are compared with skills of purely statistically based hindcasts using as predictors recently observed SSTs. For the recent 6-yr period during which real-time forecasts have been made, the skill of the raw model output is compared with that of the subjectively modified probabilistic forecasts actually issued. Despite variations from one basin to another, the levels of hindcast skill for the dynamical and statistical forecast approaches are found, overall, to be approximately equivalent at fairly modest but statistically significant levels. The dynamical forecasts require statistical postprossessing (calibration) to be competitive with, and in some circumstances superior to, the statistical models. Skill levels decrease only slowly with increasing lead time up to 2–3 months. During the recent period of real-time forecasts, the issued forecasts have had higher probabilistic skill than the raw model output, due to the forecasters’ subjective elimination of the “overconfidence” bias in the model’s forecasts. Prospects for the future improvement of dynamical tropical cyclone prediction are considered