Climate Forecast Skill Research Articles

Climate forecast skill and teleconnections on seasonal time scales over Central Africa based on the North American Multi-Model Ensemble (NMME)

Climate forecast skill and teleconnections on seasonal time scales over Central Africa based on the North American Multi-Model Ensemble (NMME)

Systematic Global Evaluation of Seasonal Climate Forecast Skill for Monthly Precipitation of JMA/MRI-CPS2 Compared with a Statistical Forecast System Using Climate Indices

In this study, we aimed to systematically and globally evaluate the monthly precipitation forecasts of JMA/MRI-CPS2, a dynamical seasonal climate forecast (Dyn-SCF) system operated by the Japan Meteorological Agency, by comparing its forecasts with those of a statistical SCF (St-SCF) system using climate indices. We developed a new global St-SCF system using 17 climate indices and compared the monthly precipitation of this system with those of JMA/MRI-CPS2. Consequently, the skill of JMA/MRI-CPS2 was determined to be globally higher than that of the St-SCF for zero-month lead forecasts. In contrast, for forecasts made with one month or longer lead times, the deterministic skill of JMA/MRI-CPS2 was comparable to that of the St-SCF and the probabilistic skill of JMA/MRI-CPS2 remained slightly higher. In addition to evaluating the skill of JMA/MRI-CPS2, we identified several regions and seasons, for which JMA/MRI-CPS2 showed a low forecast skill, compared with the St-SCF. This indicated that JMA/MRI-CPS2 cannot adequately reproduce certain dynamics. In conclusion, comparing Dyn-SCFs with St-SCFs can clarify the potential regions and seasons to improve the forecast skill of Dyn-SCFs.

Forecasting commodity returns by exploiting climate model forecasts of the El Niño Southern Oscillation

Abstract The physical and socioeconomic environments in which we live are intrinsically linked over a wide range of time and space scales. On monthly intervals, the price of many commodities produced predominantly in tropical regions covary with the dominant mode of climate variability in this region, namely the El Niño Southern Oscillation (ENSO). Here, for the spot prices returns of vegetable oils produced in Asia, we develop autoregressive (AR) models with exogenous ENSO indices, where for the first time these indices are generated by a purpose-built state-of-the-art general circulation model (GCM) climate forecasting system. The GCM is a numerical simulation which couples together the atmosphere, ocean, and sea ice, with the initial conditions tailored to maximize the climate forecast skill at multiyear timescales in the tropics. To serve as additional benchmarks, we also test commodity forecasts using: (a) no ENSO information as a lower bound; (b) perfect future ENSO knowledge as a reference upper bound; and (c) an econometric AR model of ENSO. All models adopting ENSO factors outperform those that do not, indicating the value here of incorporating climate knowledge into investment decision-making. Commodity forecasts adopting perfect ENSO factors have statistically significant skill out to 2 years. When adopting the GCM-ENSO factors, there is predictive power of the commodity beyond 1 year in the best case, which consistently outperforms commodity forecasts adopting an AR econometric model of ENSO.

Seasonal Climate Forecast Skill Assessment for the Management of Water Resources in a Run of River Hydropower System in the Poqueira River (Southern Spain)

Run of river (RoR) hydropower systems, despite being one of the most cost-effective and environmentally benign energy technologies, have the disadvantage that production is not constant because it is subject to a high variability in precipitation and snow cover. In addition, the management of RoR plants has to comply with some particular operating conditions, but also with some environmental flow requirements. This work presents the assessment of the main inputs included in a climate service, historical local data and the seasonal forecast of water inflow to RoR plants, which are used to predict the operability and the expected energy production. The analysis is presented through the application in a pilot RoR system located in the south of Spain, in a semi-arid Mediterranean area impacted by snow, where seasonal forecasting is especially challenging. The results show the high interannual variability of the operation in this kind of facilities. The outcomes indicate that seasonal climate forecast information would improve the prediction of observed river streamflow by 7.4% in reliability and 3.2% in sharpness compared to the current operational forecast based on historical data. The climate forecasts thus provide valuable information for the exploitation of available water resources, which generates a significant value for the operation of the plant and the energy production market.

Application of Postprocessing to Watershed-Scale Subseasonal Climate Forecasts over the Contiguous United States

AbstractSubseasonal to seasonal (S2S) climate forecasting has become a central component of climate services aimed at improving water management. In some cases, operational S2S climate predictions are translated into inputs for follow-on analyses or models, whereas the S2S predictions on their own may provide for qualitative situational awareness. At the spatial scales of water management, however, S2S climate forecasts often suffer from systematic biases, and low skill and reliability. This study assesses the potential to improve S2S forecast skill and salience for watershed applications through the use of postprocessing to harness skills in large-scale fields from the global climate model forecast outputs. To this end, the components-based technique—partial least squares regression (PLSR)—is used to improve the skill of biweekly temperature and precipitation forecasts from the Climate Forecast System version 2 (CFSv2). The PLSR method forms predictor components based on a cross-validated analysis of hindcasts from CFSv2 climate and land surface fields, and the results are benchmarked against raw CFSv2 forecasts, remapped to intermediate-scale watershed areas. We find that postprocessing affords marginal to moderate gains in skill in many watersheds, raising climate forecast skill above a usability threshold over the four seasons analyzed. In other locations, however, postprocessing fails to improve skill, particularly for extreme events, and can lead to unreliably narrow forecast ranges. This work presents evidence that the statistical postprocessing of climate forecast system outputs has potential to improve forecast skill, but that more thorough study of alternative approaches and predictors may be needed to achieve comprehensively positive outcomes.

Benchmark decadal forecast skill for terrestrial water storage estimated by an elasticity framework

A reliable decadal prediction of terrestrial water storage (TWS) is critical for a sustainable management of freshwater resources and infrastructures. However, the dependence of TWS forecast skill on the accuracy of initial hydrological conditions and decadal climate forecasts is not clear, and the baseline skill remains unknown. Here we use decadal climate hindcasts and perform hydrological ensemble simulations to estimate a benchmark decadal forecast skill for TWS over global major river basins with an elasticity framework that considers varying skill of initial conditions and climate forecasts. The initial condition skill elasticity is higher than climate forecast skill elasticity over many river basins at 1–4 years lead, suggesting the dominance of initial conditions at short leads. However, our benchmark skill for TWS is significantly higher than initial conditions-based forecast skill over 25 and 31% basins for the leads of 1–4 and 3–6 years, and incorporating climate prediction can significantly increase TWS prediction skill over half of the river basins at long leads, especially over mid- and high-latitudes. Our findings imply the possibility of improving decadal TWS forecasts by using dynamical climate prediction information, and the necessity of using the new benchmark skill for verifying the success of decadal hydrological forecasts.

Value of seasonal forecasting for sugarcane farm irrigation planning

Abstract Seasonal climate forecasts (SCFs) have potential to improve productivity and profitability in the sugar industry. However, they are often underutilised due to insufficient evidence of the economic value of the forecasts, especially when there is a level of uncertainty associated with SCFs. Here, we demonstrate the value of integrating SCFs at various forecast quality (skill) levels into seasonal irrigation planning for sugarcane farming. A seasonal forecast system based on ENSO (El Nino Southern Oscillation) phases was parameterised by forecast quality to predict seasonal precipitation tercile (i.e. wet, neutral and dry) categories. A bio-economic model was developed to determine water-yield-profit relationships. Sugarcane production under different climatic conditions and irrigation scheduling scenarios was simulated using the Agricultural Production Systems sIMulator (APSIM)-Sugar, calibrated using case study information from one of Australia’s major irrigated sugarcane growing regions. We then employed an expected profit approach to achieve an optimal profit, rather than the more conventional optimal yield, for plant and ratoon crops to quantify the potential value of using SCFs in sugarcane irrigation decision making. The results show that using skilled SCF systems in sugarcane irrigation decision making can help growers improve their gross margin compared to that achieved in the absence of climate information (economic value). With a perfect forecast of moderate climatic conditions, an average economic value of up to AUD 27 ha−1 per annum was achieved, while forecasts of moderate wet or dry conditions indicated gains of up to AUD 40 and 43 ha−1 per annum, respectively, and forecasts of extreme wet or dry conditions delivered economic gains of up to AUD 150 and 260 ha−1 per annum, respectively. With the current seasonal climate forecast skill of 60% (based on the ENSO phases forecasting system) in the case study region, an average gain of up to AUD 4.5 ha−1 per annum was realised, with up to AUD 6.2 and 7.1 ha−1 per annum, respectively, for moderate wet and dry forecasting and up to AUD 92 and 43 ha−1 per annum, respectively, for extreme wet and dry forecasting. Improvements in the skill and reliability of SCFs will be important for achieving greater productivity and/or profitability and the wider uptake of climate forecasts in agricultural decision making.

Oceanographic factors related to Eastern Little Tuna (Euthynnus affinis) catches in the west Java Sea

The satellite data included sea surface height anomaly (SSHA) from AVISO, sea surface temperature (SST) and chlorophyll-a (Chl-a) from Aqua MODIS and Eastern Little Tuna (Euthynnus affinis) catches were used as a combined dataset to understand the ocean variations and further addresses their relations with the Eastern Little Tuna catches in the Java Sea. The fish catches and remotely sensed data were analyzed for the 5 years datasets from 2010-2014. The relationships of oceanographic factors and catch distribution were explored with a generalized additive model (GAM). Catch rates varied temporally relatively significant over year-round. The Eastern Little Tuna catch rates have the peak season in March and October (900 ton to 1000 ton). The ELT catch peaks during the transition season from southeast to northwest monsoon (September to November) and decreases during southeast monsoon (June to August). The GAM results showed that the 3-oceanographic parameter combination models explained the highest deviance (41.4%) with Chl-a explained the highest deviance (23.3%). High probabilities of Eastern Little Tuna catches corresponded to marine productivity of Chl-a concentration ranging from 0.3-0.5 mg/m3, for SSHA ranging from 0-8 cm and SST ranging from 28-29°C. We recommend to have further investigations through the use of long-term historical time series to predict fishing ground locations and an emerging need to improve our climate understanding and forecast skill to conserve small pelagic catches.

Quantifying Streamflow Forecast Skill Elasticity to Initial Condition and Climate Prediction Skill

Abstract Water resources management decisions commonly depend on monthly to seasonal streamflow forecasts, among other kinds of information. The skill of such predictions derives from the ability to estimate a watershed’s initial moisture and energy conditions and to forecast future weather and climate. These sources of predictability are investigated in an idealized (i.e., perfect model) experiment using calibrated hydrologic simulation models for 424 watersheds that span the continental United States. Prior work in this area also followed an ensemble-based strategy for attributing streamflow forecast uncertainty, but focused only on two end points representing zero and perfect information about future forcings and initial conditions. This study extends the prior approach to characterize the influence of varying levels of uncertainty in each area on streamflow prediction uncertainty. The sensitivities enable the calculation of flow forecast skill elasticities (i.e., derivatives) relative to skill in either predictability source, which are used to characterize the regional, seasonal, and predictand variations in flow forecast skill dependencies. The resulting analysis provides insights on the relative benefits of investments toward improving watershed monitoring (through modeling and measurement) versus improved climate forecasting. Among other key findings, the results suggest that climate forecast skill improvements can be amplified in streamflow prediction skill, which means that climate forecasts may have greater benefit for monthly-to-seasonal flow forecasting than is apparent from climate forecast skill considerations alone. The results also underscore the importance of advancing hydrologic modeling, expanding watershed observations, and leveraging data assimilation, all of which help capture initial hydrologic conditions that are often the dominant influence on hydrologic predictions.

Seasonal drought predictability and forecast skill over China

AbstractUnder a changing environment, seasonal droughts have been exacerbated with devastating impacts. However, the understanding of drought mechanism and predictability is limited. Based on the hindcasts from multiple climate models, the predictability and forecast skill for drought over China are investigated. The 3 month standardized precipitation index is used as the drought index, and the predictability is quantified by using a perfect model assumption. Ensemble hindcasts from multiple climate models are assessed individually, and the grand multimodel ensemble is also evaluated. Drought forecast skill for model ensemble mean is higher than individual ensemble members, and North American Multimodel Ensemble grand ensemble performs the best. Predictability is higher than forecast skill, indicating the room for improving drought forecast. Drought predictability and forecast skill are positively correlated in general, but they vary depending on seasons, regions, and forecast leads. Higher drought predictability and forecast skill are found over regimes where ENSO has significant impact. For the ENSO‐affected regimes, both drought predictability and forecast skill in ENSO years are higher than that in neutral years. This study suggests that predictability not only provides a measure for selecting climate models for ensemble drought forecast in ENSO‐affected regimes but also serves as an indicator for forecast skill especially when in situ and/or remote sensing measurements for the hindcast verifications are considered unreliable.

On the sources of global land surface hydrologic predictability

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

High-Resolution Global Climate Simulations with the ECMWF Model in Project Athena: Experimental Design, Model Climate, and Seasonal Forecast Skill

The sensitivity to the horizontal resolution of the climate, anthropogenic climate change, and seasonal predictive skill of the ECMWF model has been studied as part of Project Athena—an international collaboration formed to test the hypothesis that substantial progress in simulating and predicting climate can be achieved if mesoscale and subsynoptic atmospheric phenomena are more realistically represented in climate models.In this study the experiments carried out with the ECMWF model (atmosphere only) are described in detail. Here, the focus is on the tropics and the Northern Hemisphere extratropics during boreal winter. The resolutions considered in Project Athena for the ECMWF model are T159 (126 km), T511 (39 km), T1279 (16 km), and T2047 (10 km). It was found that increasing horizontal resolution improves the tropical precipitation, the tropical atmospheric circulation, the frequency of occurrence of Euro-Atlantic blocking, and the representation of extratropical cyclones in large parts of the Northern Hemisphere extratropics. All of these improvements come from the increase in resolution from T159 to T511 with relatively small changes for further resolution increases to T1279 and T2047, although it should be noted that results from this very highest resolution are from a previously untested model version. Problems in simulating the Madden–Julian oscillation remain unchanged for all resolutions tested. There is some evidence that increasing horizontal resolution to T1279 leads to moderate increases in seasonal forecast skill during boreal winter in the tropics and Northern Hemisphere extratropics. Sensitivity experiments are discussed, which helps to foster a better understanding of some of the resolution dependence found for the ECMWF model in Project Athena.

Seasonal hydrologic prediction in the United States: understanding the role of initial hydrologic conditions and seasonal climate forecast skill

Abstract. Seasonal hydrologic forecasts derive their skill from knowledge of initial hydrologic conditions and climate forecast skill associated with seasonal climate outlooks. Depending on the type of hydrological regime and the season, the relative contributions of initial hydrologic conditions and climate forecast skill to seasonal hydrologic forecast skill vary. We seek to quantify these contributions on a relative basis across the Conterminous United States. We constructed two experiments – Ensemble Streamflow Prediction and reverse-Ensemble Streamflow Prediction – to partition the contributions of the initial hydrologic conditions and climate forecast skill to overall forecast skill. In ensemble streamflow prediction (first experiment) hydrologic forecast skill is derived solely from knowledge of initial hydrologic conditions, whereas in reverse-ensemble streamflow prediction (second experiment), it is derived solely from atmospheric forcings (i.e. perfect climate forecast skill). Using the ratios of root mean square error in predicting cumulative runoff and mean monthly soil moisture of each experiment, we identify the variability of the relative contributions of the initial hydrologic conditions and climate forecast skill spatially throughout the year. We conclude that the initial hydrologic conditions generally have the strongest influence on the prediction of cumulative runoff and soil moisture at lead-1 (first month of the forecast period), beyond which climate forecast skill starts to have greater influence. Improvement in climate forecast skill alone will lead to better seasonal hydrologic forecast skill in most parts of the Northeastern and Southeastern US throughout the year and in the Western US mainly during fall and winter months; whereas improvement in knowledge of the initial hydrologic conditions can potentially improve skill most in the Western US during spring and summer months. We also observed that at a short lead time (i.e. lead-1) contribution of the initial hydrologic conditions in soil moisture forecasts is more extensive than in cumulative runoff forecasts across the Conterminous US.

A multiple model assessment of seasonal climate forecast skill for applications

Skilful seasonal climate forecasts have potential to affect decision making in agriculture, health and water management. Organizations such as the National Oceanic and Atmospheric Administration (NOAA) are currently planning to move towards a climate services paradigm, which will rest heavily on skilful forecasts at seasonal (1 to 9 months) timescales from coupled atmosphere‐land‐ocean models. We present a careful analysis of the predictive skill of temperature and precipitation from eight seasonal climate forecast models with the joint distribution of observations and forecasts. Using the correlation coefficient, a shift in the conditional distribution of the observations given a forecast can be detected, which determines the usefulness of the forecast for applications. Results suggest there is a deficiency of skill in the forecasts beyond month‐1, with precipitation having a more pronounced drop in skill than temperature. At long lead times only the equatorial Pacific Ocean exhibits significant skill. This could have an influence on the planned use of seasonal forecasts in climate services and these results may also be seen as a benchmark of current climate prediction capability using (dynamic) couple models.

Improved Weather and Seasonal Climate Forecasts from Multimodel Superensemble.

A method for improving weather and climate forecast skill has been developed. It is called a superensemble, and it arose from a study of the statistical properties of a low-order spectral model. Multiple regression was used to determine coefficients from multimodel forecasts and observations. The coefficients were then used in the superensemble technique. The superensemble was shown to outperform all model forecasts for multiseasonal, medium-range weather and hurricane forecasts. In addition, the superensemble was shown to have higher skill than forecasts based solely on ensemble averaging.