A simple graphical technique for conditional long range forecasting of below-average rainfall periods in the Tuvalu Islands, Western Pacific

For the Tuvalu Island group in the western Pacific, a simple graphical method is proposed as a means of forecasting whether rainfall totals for the next 1, 2,…,6 months will be below average. The method is based on scatter plots where the points are color-coded as above- or below-average rainfall, with the plot axes being lag-1 and lag-2 NINO4 sea surface temperatures. Within the scatter plots there are reasonably clear data fields with higher frequencies of below-average rainfalls associated with cooler precursor NINO4 temperatures. These data fields are defined by subjectively emplaced separation lines which bifurcate the scatter plots into “predictable” and “unpredictable” fields. If two precursor NINO4 temperature values define a point located in a predictable field then a warning would be issued for below-average rainfall over the next n-month period, depending on the time scale of the scatter plot. A long rainfall record at Funafuti in Tuvalu indicates that success in predictable-field forecasting of below-average rainfalls would range between 68% for 1-month rainfall totals and 89% for 6-month totals. The forecasting success derives from persistence of cooler NINO4 sea surface temperatures which are associated with lower rainfalls in Tuvalu. However, many dry periods are also located in the unpredictable field and cannot be forecast by this method

Seasonal prediction of lake inflows and rainfall in a hydro-electricity catchment, Waitaki river, New Zealand

The Waitaki River is located in the centre of the South Island of New Zealand, and hydro-electricity generated on the river accounts for 35-40% of New Zealand's electricity. Low inflows in 1992 and 2001 resulted in the threat of power blackouts. Improved seasonal rainfall and inflow forecasts will result in the better management of the water used in hydro-generation on a seasonal basis. Researchers have stated that two key directions in the fields of seasonal rainfall and streamflow forecasting are to a) decrease the spatial scale of forecast products, and b) tailor forecast products to end-user needs, so as to provide more relevant and targeted forecasts. Several season-ahead lake inflow and rainfall forecast models were calibrated for the Waitaki river catchment using statistical techniques to quantify relationships between land-ocean-atmosphere state variables and seasonally lagged inflows and rainfall. Techniques included principal components analysis and multiple linear regression, with cross-validation techniques applied to estimate model error and randomization techniques used to establish the significance of the skill of the models. Many of the models calibrated predict rainfall and inflows better than random chance and better than the long-term mean as a predictor. When compared to the range of all probable inflow seasonal totals (based on the 80-year recorded history in the catchment), 95% confidence limits around most model predictions offer significant skill. These models explain up to 19% of the variance in season-ahead rainfall and inflows in this catchment. Seasonal rainfall and inflow forecasting on a single catchment scale and focussed to end-user needs is possible with some skill in the South Island of New Zealand

CHALLENGES AND OPPORTUNITIES PROVIDED BY SEASONAL CLIMATE FORECASTS: A LITERATURE REVIEW

Use of seasonal climate forecasts is a rapidly evolving area. Effective research and application of climate forecasts require close cooperation between scientists in diverse disciplines and decision makers. Successful collaboration requires all players to at least partially understand each other's perspectives. Issues associated with seasonal forecasts, through a selected review of both physical and social sciences literature, is presented. Our hope is that the review will improve research in this area by stimulating further collaborations.climate forecasts, review, value of information, Resource /Energy Economics and Policy, D80, D81, O30, Q00,

Current Approaches to Seasonal to Interannual Climate Predictions

This review paper presents an assessment of the current state of knowledge and capability in seasonal climate prediction at the end of the 20th century. The discussion covers the full range of issues involved in climate forecasting, including (1) the theory and empirical evidence for predictability; (2) predictions of surface boundary conditions, such as sea surface temperatures (SSTs) that drive the predictable part of the climate; (3) predictions of the climate; and (4) a brief consideration of the application of climate forecasts. Within this context, the research of the coming decades that seeks to address shortcomings in each area is described

Robust nonlinear canonical correlation analysis: application to seasonal climate forecasting

International audienceRobust variants of nonlinear canonical correlation analysis (NLCCA) are introduced to improve performance on datasets with low signal-to-noise ratios, for example those encountered when making seasonal climate forecasts. The neural network model architecture of standard NLCCA is kept intact, but the cost functions used to set the model parameters are replaced with more robust variants. The Pearson product-moment correlation in the double-barreled network is replaced by the biweight midcorrelation, and the mean squared error (mse) in the inverse mapping networks can be replaced by the mean absolute error (mae). Robust variants of NLCCA are demonstrated on a synthetic dataset and are used to forecast sea surface temperatures in the tropical Pacific Ocean based on the sea level pressure field. Results suggest that adoption of the biweight midcorrelation can lead to improved performance, especially when a strong, common event exists in both predictor/predictand datasets. Replacing the mse by the mae leads to improved performance on the synthetic dataset, but not on the climate dataset except at the longest lead time, which suggests that the appropriate cost function for the inverse mapping networks is more problem dependent

A simple forecasting scheme for predicting low rainfalls in Funafuti, Tuvalu

The development of some ability for forecasting low rainfalls would be helpful in Tuvalu as rainwater is the only source of fresh water in the country. The subsurface water is brackish and saline so the entire country depends totally on rainwater for daily domestic supplies, agricultural and farming activities. More importantly, these atolls are often influenced by droughts which consequently make inadequate drinking water an issue. A simple graph-based forecasting scheme is developed and presented in this thesis for forecasting below average mean rainfall in Funafuti over the next n-month period. The approach uses precursor ocean surface temperature data to make predictions of below average rainfall for n = 1, 2 12. The simplicity of the approach makes it a suitable method for the country and thus for the Tuvalu Meteorological Service to use as an operational forecasting tool in the climate forecasting desk. The graphical method was derived from standardised monthly rainfalls from the Funafuti manual raingauge for the period January 1945 to July 2007. The method uses lag-1 and-lag 2 NINO4 sea surface temperatures to define whether prediction conditions hold. The persistence of predictability tends to be maintained when the observed NINO4 ocean surface temperatures fall below 26.0oC. Although the developed method has a high success probability of up to 80 percent, this can only be achieved when conditions are within the predictable field. A considerable number of below average rainfall periods are not within the predictable field and therefore cannot be forecast by this method. However, the graphical approach has particular value in warning when an existing drought is likely to continue

Current approaches to season-to-interannual climate prediction

This review paper presents an assessment of the current state of knowledge and capability in seasonal climate prediction at the end of the 20th century. The discussion covers the full range of issues involved in climate forecasting, including (1) the theory and empirical evidence for predictability; (2) predictions of surface boundary conditions, such as sea surface temperatures (SSTs) that drive the predictable part of the climate; (3) predictions of the climate; and (4) a brief consideration of the application of climate forecasts. Within this context, the research of the coming decades that seeks to address shortcomings in each area is described

Application of neural networks to subseasonal to seasonal predictability in present and future climates

Includes bibliographical references.2022 Fall.The Earth system is known for its lack of predictability on subseasonal to seasonal timescales (S2S; 2 weeks to a season). Yet accurate predictions on these timescales provide crucial, actionable lead times for agriculture, energy, and water management sectors. Fortunately, specific Earth system states – deemed forecasts of opportunity – can be leveraged to improve prediction skill. Our current understanding of these opportunities are rooted in our knowledge of the historical climate. Depending on societal actions, the future climate could vary drastically, and these possible futures could lead to varying changes to S2S predictability. In recent years, neural networks have been successfully applied to weather and climate prediction. With the rapid development of neural network explainability techniques, the application of neural networks now provides an opportunity to further understand our climate system as well. The research presented here demonstrates the utility of explainable neural networks for S2S prediction and predictability changes under future climates. The first study presents a novel approach for identifying forecasts of opportunity in observations using neural network confidence. It further demonstrates that neural networks can be used to gain physical insight into predictability, through neural network explainability techniques. We then employ this methodology to explore S2S predictability differences in two future scenarios: under anthropogenic climate change and stratospheric aerosol injection (SAI). In particular, we explore subseasonal predictability and forecasts of opportunity changes under anthropogenic warming compared to a historical climate in the CESM2-LE. We then investigate how future seasonal predictability may differ under SAI compared to a future without SAI deployment in the ARISE-SAI simulations. We find differences in predictability between the historical and future climates and the two future scenarios, respectively, where the largest differences in skill generally occur during forecasts of opportunity. This demonstrates that the forecast of opportunity approach, presented in the first study, is useful for identifying differences in future S2S predictability that may not have been identified if examining predictability across all predictions. Overall, these results demonstrate that neural networks are useful tools for exploring subseasonal to seasonal predictability, its sources, and future changes