Information Content of Seasonal Forecasts in a Changing Climate

We study the potential value to stakeholders of probabilistic long-term forecasts, as quantified by the mean information gain of the forecast compared to climatology. We use as a case study the USA Climate Prediction Center (CPC) forecasts of 3-month temperature and precipitation anomalies made at 0.5-month lead time since 1995. Mean information gain was positive but low (about 2% and 0.5% of the maximum possible for temperature and precipitation forecasts, resp.) and has not increased over time. Information-based skill scores showed similar patterns to other, non-information-based, skill scores commonly used for evaluating seasonal forecasts but tended to be smaller, suggesting that information gain is a particularly stringent measure of forecast quality. We also present a new decomposition of forecast information gain into Confidence, Forecast Miscalibration, and Climatology Miscalibration components. Based on this decomposition, the CPC forecasts for temperature are on average underconfident while the precipitation forecasts are overconfident. We apply a probabilistic trend extrapolation method to provide an improved reference seasonal forecast, compared to the current CPC procedure which uses climatology from a recent 30-year period. We show that combining the CPC forecast with the probabilistic trend extrapolation more than doubles the mean information gain, providing one simple avenue for increasing forecast skill

Ensemble Forecasts: Probabilistic Seasonal Forecasts Based on a Model Ensemble

Ensembles of general circulation model (GCM) integrations yield predictions for meteorological conditions in future months. Such predictions have implicit uncertainty resulting from model structure, parameter uncertainty, and fundamental randomness in the physical system. In this work, we build probabilistic models for long-term forecasts that include the GCM ensemble values as inputs but incorporate statistical correction of GCM biases and different treatments of uncertainty. Specifically, we present, and evaluate against observations, several versions of a probabilistic forecast for gridded air temperature 1 month ahead based on ensemble members of the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2). We compare the forecast performance against a baseline climatology based probabilistic forecast, using average information gain as a skill metric. We find that the error in the CFSv2 output is better represented by the climatological variance than by the distribution of ensemble members because the GCM ensemble sometimes suffers from unrealistically little dispersion. Lack of ensemble spread leads a probabilistic forecast whose variance is based on the ensemble dispersion alone to underperform relative to a baseline probabilistic forecast based only on climatology, even when the ensemble mean is corrected for bias. We also show that a combined regression based model that includes climatology, temperature from recent months, trend, and the GCM ensemble mean yields a probabilistic forecast that outperforms approaches using only past observations or GCM outputs. Improvements in predictive skill from the combined probabilistic forecast vary spatially, with larger gains seen in traditionally hard to predict regions such as the Arctic

A global empirical typology of anthropogenic drivers of environmental change in deltas

It is broadly recognized that river delta systems around the world are under threat from a range of anthropogenic activities. These activities occur at the local delta scale, at the regional river and watershed scale, and at the global scale. Tools are needed to support generalization of results from case studies in specific deltas. Here, we present a methodology for quantitatively constructing an empirical typology of anthropogenic change in global deltas. Utilizing a database of environmental change indicators, each associated with increased relative sea-level rise and coastal wetland loss, a clustering analysis of 48 global deltas provides a quantitative assessment of systems experiencing similar or dissimilar sources and degrees of anthropogenic stress. By identifying quantitatively similar systems, we hope to improve the transferability of scientific results across systems, and increase the effectiveness of delta management best practices. Both K-Means and Affinity Propagation clustering algorithms find similar clusters, with relative stability across small changes in KMeans cluster number. High-latitude deltas appear similar, in terms of anthropogenic environmental stress, to several low-population, low-latitude systems, including the Amazon delta, despite substantially different climatic regimes. Highly urbanized deltas in Southeast Asia form a distinct cluster. By providing a quantitative boundary between groups of delta systems, this approach may also be useful for assessing future delta change and sustainability given projected population growth, urbanization, and economic development trends

Ensemble Forecasts: Probabilistic Seasonal Forecasts Based on a Model Ensemble

Ensembles of general circulation model (GCM) integrations yield predictions for meteorological conditions in future months. Such predictions have implicit uncertainty resulting from model structure, parameter uncertainty, and fundamental randomness in the physical system. In this work, we build probabilistic models for long-term forecasts that include the GCM ensemble values as inputs but incorporate statistical correction of GCM biases and different treatments of uncertainty. Specifically, we present, and evaluate against observations, several versions of a probabilistic forecast for gridded air temperature 1 month ahead based on ensemble members of the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2). We compare the forecast performance against a baseline climatology based probabilistic forecast, using average information gain as a skill metric. We find that the error in the CFSv2 output is better represented by the climatological variance than by the distribution of ensemble members because the GCM ensemble sometimes suffers from unrealistically little dispersion. Lack of ensemble spread leads a probabilistic forecast whose variance is based on the ensemble dispersion alone to underperform relative to a baseline probabilistic forecast based only on climatology, even when the ensemble mean is corrected for bias. We also show that a combined regression based model that includes climatology, temperature from recent months, trend, and the GCM ensemble mean yields a probabilistic forecast that outperforms approaches using only past observations or GCM outputs. Improvements in predictive skill from the combined probabilistic forecast vary spatially, with larger gains seen in traditionally hard to predict regions such as the Arctic

Information Content of Seasonal Forecasts in a Changing Climate

We study the potential value to stakeholders of probabilistic long-term forecasts, as quantified by the mean information gain of the forecast compared to climatology. We use as a case study the USA Climate Prediction Center (CPC) forecasts of 3-month temperature and precipitation anomalies made at 0.5-month lead time since 1995. Mean information gain was positive but low (about 2% and 0.5% of the maximum possible for temperature and precipitation forecasts, resp.) and has not increased over time. Information-based skill scores showed similar patterns to other, non-information-based, skill scores commonly used for evaluating seasonal forecasts but tended to be smaller, suggesting that information gain is a particularly stringent measure of forecast quality. We also present a new decomposition of forecast information gain into Confidence, Forecast Miscalibration, and Climatology Miscalibration components. Based on this decomposition, the CPC forecasts for temperature are on average underconfident while the precipitation forecasts are overconfident. We apply a probabilistic trend extrapolation method to provide an improved reference seasonal forecast, compared to the current CPC procedure which uses climatology from a recent 30-year period. We show that combining the CPC forecast with the probabilistic trend extrapolation more than doubles the mean information gain, providing one simple avenue for increasing forecast skill

A global empirical typology of anthropogenic drivers of environmental change in deltas

It is broadly recognized that river delta systems around the world are under threat from a range of anthropogenic activities. These activities occur at the local delta scale, at the regional river and watershed scale, and at the global scale. Tools are needed to support generalization of results from case studies in specific deltas. Here, we present a methodology for quantitatively constructing an empirical typology of anthropogenic change in global deltas. Utilizing a database of environmental change indicators, each associated with increased relative sea-level rise and coastal wetland loss, a clustering analysis of 48 global deltas provides a quantitative assessment of systems experiencing similar or dissimilar sources and degrees of anthropogenic stress. By identifying quantitatively similar systems, we hope to improve the transferability of scientific results across systems, and increase the effectiveness of delta management best practices. Both K-Means and Affinity Propagation clustering algorithms find similar clusters, with relative stability across small changes in KMeans cluster number. High-latitude deltas appear similar, in terms of anthropogenic environmental stress, to several low-population, low-latitude systems, including the Amazon delta, despite substantially different climatic regimes. Highly urbanized deltas in Southeast Asia form a distinct cluster. By providing a quantitative boundary between groups of delta systems, this approach may also be useful for assessing future delta change and sustainability given projected population growth, urbanization, and economic development trends