Performance of GCMs for seasonal prediction over India��a case study for 2009 monsoon

The 2009 drought in India was one of the major droughts that the country faced in the last 100?years. This study describes the anomalous features of 2009 summer monsoon and examines real-time seasonal predictions made using six general circulation models (GCMs). El Ni?o conditions evolved in the Pacific Ocean, and sea surface temperatures (SSTs) over the Indian Ocean were warmer than normal during monsoon 2009. The observed circulation patterns indicate a weaker monsoon in that year over India with weaker than normal convection over the Bay of Bengal and Indian landmass. Skill of the GCMs during hindcast period shows that neither these models simulate the observed interannual variability nor their multi-model ensemble (MME) significantly improves the skill of monsoon rainfall predictions. Except for one model used in this study, the real-time predictions with longer lead (2- and 1-month lead) made for the 2009 monsoon season did not provide any indication of a highly anomalous monsoon. However, with less lead time (zero lead), most of the models as well as the MME had provided predictions of below normal rainfall for that monsoon season. This study indicates that the models could not predict the 2009 drought over India due to the use of less warm SST anomalies over the Pacific in the longer lead runs. Hence, it is proposed that the uncertainties in SST predictions (the lower boundary condition) have to be represented in the model predictions of summer monsoon rainfall over India.     

How are seasonal prediction skills related to models’ performance on mean state and annual cycle?

Given observed initial conditions, how well do coupled atmosphere–ocean models predict precipitation climatology with 1-month lead forecast? And how do the models’ biases in climatology in turn affect prediction of seasonal anomalies? We address these questions based on analysis of 1-month lead retrospective predictions for 21 years of 1981–2001 made by 13 state-of-the-art coupled climate models and their multi-model ensemble (MME). The evaluation of the precipitation climatology is based on a newly designed metrics that consists of the annual mean, the solstitial mode and equinoctial asymmetric mode of the annual cycle, and the rainy season characteristics. We find that the 1-month lead seasonal prediction made by the 13-model ensemble has skills that are much higher than those in individual model ensemble predictions and approached to those in the ERA-40 and NCEP-2 reanalysis in terms of both the precipitation climatology and seasonal anomalies. We also demonstrate that the skill for individual coupled models in predicting seasonal precipitation anomalies is positively correlated with its performances on prediction of the annual mean and annual cycle of precipitation. In addition, the seasonal prediction skill for the tropical SST anomalies, which are the major predictability source of monsoon precipitation in the current coupled models, is closely link to the models’ ability in simulating the SST mean state. Correction of the inherent bias in the mean state is critical for improving the long-lead seasonal prediction. Most individual coupled models reproduce realistically the long-term annual mean precipitation and the first annual cycle (solstitial mode), but they have difficulty in capturing the second annual (equinoctial asymmetric) mode faithfully, especially over the Indian Ocean (IO) and Western North Pacific (WNP) where the seasonal cycle in SST has significant biases. The coupled models replicate the monsoon rain domains very well except in the East Asian subtropical monsoon and the tropical WNP summer monsoon regions. The models also capture the gross features of the seasonal march of the rainy season including onset and withdraw of the Asian–Australian monsoon system over four major sub-domains, but striking deficiencies in the coupled model predictions are observed over the South China Sea and WNP region, where considerable biases exist in both the amplitude and phase of the annual cycle and the summer precipitation amount and its interannual variability are underestimated.     

Intensification of seasonal extremes given a 2°C global warming target

Current international efforts to reduce greenhouse gas emissions and limit human-induced global-mean near-surface temperature increases to 2°C, relative to the pre-industrial era, are intended to avoid possibly significant and dangerous impacts to physical, biological, and socio-economic systems. However, it is unknown how these various systems will respond to such a temperature increase because their relevant spatial scales are much different than those represented by numerical global climate models—the standard tool for climate change studies. This deficiency can be addressed by using higher-resolution regional climate models, but at great computational expense. The research presented here seeks to determine how a 2°C global-mean temperature increase might change the frequency of seasonal temperature extremes, both in the United States and around the globe, without necessarily resorting to these computationally-intensive model experiments. Results indicate that in many locations the regional temperature increases that accompany a 2°C increase in global mean temperatures are significantly larger than the interannual-to-decadal variations in seasonal-mean temperatures; in these locations a 2°C global mean temperature increase results in seasonal-mean temperatures that consistently exceed the most extreme values experienced during the second half of the 20th Century. Further, results indicate that many tropical regions, despite having relatively modest overall temperature increases, will have the most substantial increase in number of hot extremes. These results highlight that extremes very well could become the norm, even given the 2°C temperature increase target.     

Spatial prediction of urban�Crural temperatures using statistical methods

Spatial information on climatic characteristics is beneficial in e.g. regional planning, building construction and urban ecology. The possibility to spatially predict urban?Crural temperatures with statistical techniques and small sample sizes was investigated in Turku, SW Finland. Temperature observations from 36 stationary weather stations over a period of 6?years were used in the analyses. Geographical information system (GIS) data on urban land use, hydrology and topography served as explanatory variables. The utilized statistical techniques were generalized linear model and boosted regression tree method. The results demonstrate that temperature variables can be robustly predicted with relatively small sample sizes (n????20?C40). The variability in the temperature data was explained satisfactorily with few accessible GIS variables. Statistically based spatial modelling provides a cost-efficient approach to predict temperature variables on a regional scale. Spatial modelling may aid also in gaining novel insights into the causes and impacts of temperature variability in extensive urbanized areas.     

What determines perceived value of seasonal climate forecasts? A theoretical analysis

Seasonal forecasts have potential value as tools for the management of risks due to inter-annual climate variability and iterative adaptation to climate change. Despite their potential, forecasts are not widely used, in part due to poor performance and lack of relevance to specific users’ decision problems, and in part due to a variety of economic and behavioural factors. In this paper a theoretical model of perceived forecast value is proposed and applied to a stylized portfolio-type decision problem with wide applicability to actual forecast users, with a view to obtaining a more complete picture of the determinants of perceived value. The effects of user wealth, risk aversion, and perceived forecast trustworthiness, and presentational parameters, such as the position of forecast parameter categories, and the size of probability categories, on perceived value is investigated. Analysis of the model provides several strong qualitative predictions of how perceived forecast value depends on these factors. These predictions may be used to generate empirical hypotheses which offer the chance of evaluating the model's assumptions, and suggest several means of improving understanding of perceived value based on qualitative features of the results.     

An evaluation of extrapolation techniques for the short‐term prediction of rain amounts

Abstract

Twenty‐seven radar cells from the Tropical Atlantic observed during GATE were followed and measurements of their fluxes and areas for initial time increments T₀ were fitted to various extrapolation schemes. The extrapolation procedure that gave the smallest error inforecasting the changes influx and area, was found to be the linear one and the optimum increment T₀ was about 30 min. However, even though these techniques have the advantage of establishing a trend in the behaviour of the flux and area with time, a comparison of the forecast errors from the linear extrapolation scheme with those from the “status quo” (persistence) assumption shows little if any improvement.

A technique including both cell motion and internal changes influx and area of the rain cells was developed to evaluate the accuracy of rain accumulation forecasts. It was found that the errors generated by the “status quo” assumption were of the order of 77% for a 2‐h forecast with little improvement by allowing for the extrapolation of area and flux.     

Current status of ENSO prediction skill in coupled ocean–atmosphere models

The overall skill of ENSO prediction in retrospective forecasts made with ten different coupled GCMs is investigated. The coupled GCM datasets of the APCC/CliPAS and DEMETER projects are used for four seasons in the common 22 years from 1980 to 2001. As a baseline, a dynamic-statistical SST forecast and persistence are compared. Our study focuses on the tropical Pacific SST, especially by analyzing the NINO34 index. In coupled models, the accuracy of the simulated variability is related to the accuracy of the simulated mean state. Almost all models have problems in simulating the mean and mean annual cycle of SST, in spite of the positive influence of realistic initial conditions. As a result, the simulation of the interannual SST variability is also far from perfect in most coupled models. With increasing lead time, this discrepancy gets worse. As one measure of forecast skill, the tier-1 multi-model ensemble (MME) forecasts of NINO3.4 SST have an anomaly correlation coefficient of 0.86 at the month 6. This is higher than that of any individual model as well as both forecasts based on persistence and those made with the dynamic-statistical model. The forecast skill of individual models and the MME depends strongly on season, ENSO phase, and ENSO intensity. A stronger El Niño is better predicted. The growth phases of both the warm and cold events are better predicted than the corresponding decaying phases. ENSO-neutral periods are far worse predicted than warm or cold events. The skill of forecasts that start in February or May drops faster than that of forecasts that start in August or November. This behavior, often termed the spring predictability barrier, is in part because predictions starting from February or May contain more events in the decaying phase of ENSO.     

Energy balance of the intertidal zone of Western Hudson bay II: Ice‐dominated periods and seasonal patterns

Abstract

This study treats the energy balance during fast‐ice and floating‐ice conditions and examines overall seasonal patterns. The rate of ablation of the fast ice was controlled equally by net radiation and air temperature. The ratio of net/solar radiation increased 2.5 times during the ablation period owing to the decrease in ice albedo. Air temperature in the ablation zone was up to 8°C colder than that over the adjacent snow‐free terrestrial surface and remained near 0°Cfor the full ablation period. The sensible heat flux was small and downward (negative), whereas the evaporative heat flux was small and positive. Thus, the energy used in melting the ice was approximately equal to that provided by the net radiation. Above‐freezing air temperatures decreased the albedo through surface melting thus increasing net radiation. This combination of higher temperature and large net radiation was associated with offshore winds and resulted in large ablation relative to periods with colder onshore winds.

The floating‐ice period is one of great variability owing to changing ice conditions, variable current behaviour, tidal cycles and changing wind direction. The intertidal zone acts as a major heat sink, both early and late in the floating‐ice period. The turbulent heat fluxes were small and were either positive or negative. Nearly all of the energy from net radiation was used in melting ice and in warming tidal water during high tide and in warming the residual tidal ponds and in melting stranded ice rafts during low tide.

The overall study period, from May to September, included most of the season of positive radiation balance and above‐freezing temperatures. Winds were dominantly onshore in the first half of the period and equally onshore and offshore in the second half. Wind frequencies resembled longer term averages for other stations on James Bay and Hudson Bay. The ratio of net to solar radiation was at a maximum during the ice‐free period in August, whereas for adjacent terrestrial surfaces, it was largest at the summer solstice. Land‐sea breezes first developed in mid‐July and were influential in making offshore winds the dominant nocturnal regime. As a result, offshore winds were associated with small magnitudes of net radiation. Onshore winds were more than 5°C colder than those blowing offshore and their vapour pressure deficits were three times smaller. Convective heat fluxes were small for onshore winds and very small and usually negative for offshore winds. For all wind directions throughout the period, most of the available radiant energy was used to melt ice and to heat the sea water. This is a pattern similar to that of the ice‐covered or open sea and dissimilar to that of the adjacent terrestrial environment. It implies that the main energy‐balance transitions, during onshore airflow, occur at the high‐tide line.     

Estimation of dependence of seasonal predictability of meteorological quantities in different regions of the Northern Hemisphere on the El Niño-Southern Oscillation phenomenon

Potential predictability and rms errors of ensemble forecasting of seasonal variability of the meteorological fields are estimated for different El Niño-Southern Oscillation (ENSO) episodes in the winter and summer of 1983–2002. By means of composite analysis, the rms errors and the potential predictability index are compared for different ENSO episodes in seven regions of the Northern Hemisphere midlatitudes. It is shown that the ENSO signals have a model-resolved response in the atmospheric circulation patterns in certain extratropical regions for some meteorological quantities. Geographic distribution of the potential predictability index shows that the maximum values occur near the centers of action of the main teleconnection systems.     

Prediction of the Madden�CJulian oscillation with the POAMA dynamical prediction system

Predictions of the Madden?CJulian oscillation (MJO) are assessed using a 10-member ensemble of hindcasts from POAMA, the Australian Bureau of Meteorology coupled ocean?Catmosphere seasonal prediction system. The ensemble of hindcasts was initialised from observed atmosphere and ocean initial conditions on the first of each month during 1980?C2006. The MJO is diagnosed using the Wheeler-Hendon Real-time Multivariate MJO (RMM) index, which involves projection of daily data onto the leading pair of eigenmodes from an analysis of zonal winds at 200 and 850?hPa and outgoing longwave radiation (OLR) averaged about the equator. Forecasts of the two component (RMM1 and RMM2) index are quantitatively compared with observed behaviour derived from NCEP reanalyses and satellite OLR using the bivariate correlation skill, root-mean-square error (RMSE), and measures of the MJO amplitude and phase error. Comparison is also made with a simple vector autoregressive (VAR) prediction model of RMM as a benchmark. Using the full hindcast set, we find that the MJO can be predicted with the POAMA ensemble out to about 21?days as measured by the bivariate correlation exceeding 0.5 and the bivariate RMSE remaining below ~1.4 (which is the value for a climatological forecast). The VAR model, by comparison, drops to a correlation of 0.5 by about 12?days. The prediction limit from POAMA increases by less than 2?days for times when the MJO has large initial amplitude, and has little sensitivity to the initial phase of the MJO. The VAR model, on the other hand, shows a somewhat larger increase in skill for times of strong MJO variability and has greater sensitivity to initial phase, with lower skill for times when MJO convection is developing in the Indian Ocean. The sensitivity to season is, however, greater for POAMA, with maximum skill occurring in the December?CJanuary?CFebruary season and minimum skill in June?CJuly?CAugust. Examination of the MJO amplitudes shows that individual POAMA members have slightly above observed amplitude after a spin-up of about 10?days, whereas examination of the MJO phase error reveals that the model has a consistent tendency to propagate the MJO slightly slower than observed. Finally, an estimate of potential predictability of the MJO in POAMA hindcasts suggests that actual MJO prediction skill may be further improved through continued development of the dynamical prediction system.     

Ice‐band characteristics of the Antarctic seasonal ice zone observed using MOS MESSR images

Abstract

Ice‐band characteristics for the region off East Queen Maud Land in Antarctica were examined and their relationship with the wind conditions was assessed using a large number of Marine Observation Satellite (MOS) Multispectral Electronic Self Scanning Radiometer (MESSR) images received at Syowa Station during the period 1989–93. Analyses from 43 examples of bands captured from August to December suggest that ice‐band formation and band scale are affected by both wind speed and direction over approximately the preceding four days (defined as the effective wind). Ice‐band width and spacing are significantly correlated with the effective wind speed and the maximum wind speed during that period. The long axis of ice bands tends to be oriented at 70°‐90° (mean of 75°) to the right of the effective wind direction. The band scales decrease from winter (August) to summer (December) with typical band spacing of 4–6 km in winter and 1–2 km in summer. This seems to be primarily due to a decrease in ice floe size and partly due to a decrease in the effective wind speed from winter to summer. Band scale decreases from the ice interior to the ice edge under conditions of off‐ice winds.     

A decision‐analytic study of the joint value of seasonal precipitation and temperature forecasts in a choice‐of‐crop problem

Abstract

A static decision‐analytic method is used to investigate the economic value of bivariate ‐ precipitation and temperature ‐ seasonal forecasts of the form currently issued by the U.S. National Weather Service. This method is applied to a corn versus spring wheat choice‐of‐crop decision‐making problem by considering a transect of four counties across the northwestern margin of the North American corn belt. Numerical results indicate that seasonal forecasts of current quality can be of appreciable value (≥$1/ha) for some locations when the optimal action chosen on the basis of climatological information is only marginally preferred to another action. Increases in forecast value follow from hypothetical increases in the quality of both the precipitation and temperature components of the forecasts in the spring wheat region, whereas forecast value increases primarily as a function of the quality of the precipitation forecasts alone in the corn belt region. The results are very sensitive to absolute and relative crop prices.     

Future frost risks in the Tohoku region of Japan under a warming climate—interpretation of regional diversity in terms of seasonal warming

Theoretical and Applied Climatology - We investigated future frost risks in the Tohoku Region of Japan under climate change. We focused on the processes governing regional variations in the future...     

Relevance of soil moisture for seasonal atmospheric predictions: is it an initial value problem?

Ensembles of boreal summer atmospheric simulations, spanning a 15-year period (1979–1993), are performed with the ARPEGE climate model to investigate the influence of soil moisture on climate variability and potential predictability. All experiments are forced with observed monthly mean sea surface temperatures. In addition to a control experiment with interactive soil moisture boundary conditions, two sensitivity experiments are performed. In the first, the interannual variability of the deep soil moisture is removed during the whole season, through a relaxation toward the monthly mean model climatology. In the second, only the variability of the initial soil moisture conditions is suppressed. While it is shown that soil moisture strongly contributes to the climate variability simulated in the control experiment, an analysis of variance indicates that soil moisture does not represent a significant source of predictability in most continental areas. The main exception is the North American continent, where climate predictability is clearly reduced through the use of climatological initial conditions. Using climatological soil moisture boundary conditions does not lead to strong and homogeneous impacts on potential predictability, thereby suggesting that the climate signals driven by the sea surface temperature variability are not generally amplified by interactive soil moisture and that the relevance of soil moisture for seasonal forecasting is mainly an initial value problem.     

Seasonal prediction of global sea level anomalies using an ocean–atmosphere dynamical model

Advanced warning of extreme sea level events is an invaluable tool for coastal communities, allowing the implementation of management policies and strategies to minimise loss of life and infrastructure damage. This study is an initial attempt to apply a dynamical coupled ocean–atmosphere model to the prediction of seasonal sea level anomalies (SLA) globally for up to 7 months in advance. We assess the ability of the Australian Bureau of Meteorology’s operational seasonal dynamical forecast system, the Predictive Ocean Atmosphere Model for Australia (POAMA), to predict seasonal SLA, using gridded satellite altimeter observation-based analyses over the period 1993–2010 and model reanalysis over 1981–2010. Hindcasts from POAMA are based on a 33-member ensemble of seasonal forecasts that are initialised once per month for the period 1981–2010. Our results show POAMA demonstrates high skill in the equatorial Pacific basin and consistently exhibits more skill globally than a forecast based on persistence. Model predictability estimates indicate there is scope for improvement in the higher latitudes and in the Atlantic and Southern Oceans. Most characteristics of the asymmetric SLA fields generated by El-Nino/La Nina events are well represented by POAMA, although the forecast amplitude weakens with increasing lead-time.     

Effects of air-sea interaction on extended-range prediction of geopotential height at 500 hPa over the northern extratropical region

The contribution of air-sea interaction on the extended-range prediction of geopotential height at 500 hPa in the northern extratropical region has been analyzed with a coupled model form Beijing Climate Center and its atmospheric components. Under the assumption of the perfect model, the extended-range prediction skill was evaluated by anomaly correlation coefficient (ACC), root mean square error (RMSE), and signal-to-noise ratio (SNR). The coupled model has a better prediction skill than its atmospheric model, especially, the air-sea interaction in July made a greater contribution for the improvement of prediction skill than other months. The prediction skill of the extratropical region in the coupled model reaches 16–18 days in all months, while the atmospheric model reaches 10–11 days in January, April, and July and only 7–8 days in October, indicating that the air-sea interaction can extend the prediction skill of the atmospheric model by about 1 week. The errors of both the coupled model and the atmospheric model reach saturation in about 20 days, suggesting that the predictable range is less than 3 weeks.     

Near-term increase in frequency of seasonal temperature extremes prior to the 2°C global warming target

Given current international efforts to reduce greenhouse gas emissions and limit human-induced global-mean near-surface temperature increases to 2°C, relative to the pre-industrial era, we seek to determine the impact such a temperature increase might have upon the frequency of seasonal-mean temperature extremes; further we seek to determine what global-mean temperature increase would prevent extreme temperature values from becoming the norm. Results indicate that given a 2°C global mean temperature increase it is expected that for 70–80% of the land surface maximum seasonal-mean temperatures will exceed historical extremes (as determined from the 95th percentile threshold value over the second half of the 20th Century) in at least half of all years, i.e. the current historical extreme values will effectively become the norm. Many regions of the globe—including much of Africa, the southeastern and central portions of Asia, Indonesia, and the Amazon—will reach this point given the “committed” future global-mean temperature increase of 0.6°C (1.4°C relative to the pre-industrial era) and 50% of the land surface will reach it given a future global-mean temperature increase of between 0.8 and 0.95°C (1.6–1.75°C relative to the pre-industrial era). These results suggest substantial fractions of the globe could experience seasonal-mean temperature extremes with high regularity, even if the global-mean temperature increase remains below the 2°C target.     

Multi-scheme corrected dynamic–analogue prediction of summer precipitation in northeastern China based on BCC_CSM

Based on summer precipitation hindcasts for 1991–2013 produced by the Beijing Climate Center Climate System Model (BCC_CSM), the relationship between precipitation prediction error in northeastern China (NEC) and global sea surface temperature is analyzed, and dynamic–analogue prediction is carried out to improve the summer precipitation prediction skill of BCC_CSM, through taking care of model historical analogue prediction error in the real-time output. Seven correction schemes such as the systematic bias correction, pure statistical correction, dynamic–analogue correction, and so on, are designed and compared. Independent hindcast results show that the 5-yr average anomaly correlation coefficient (ACC) of summer precipitation is respectively improved from –0.13/0.15 to 0.16/0.24 for 2009–13/1991–95 when using the equally weighted dynamic–analogue correction in the BCC_CSM prediction, which takes the arithmetical mean of the correction based on regional average error and that on grid point error. In addition, probabilistic prediction using the results from the multiple correction schemes is also performed and it leads to further improved 5-yr average prediction accuracy.