December January February Season Research Articles

Replicating the Hadley cell edge and subtropical jet latitude disconnect in idealized atmospheric models

Abstract. Recent work has shown that variability in the subtropical jet's (STJ) latitude, ϕSTJ, is not coupled to that of the Hadley cell (HC) edge, ϕHC, but the robustness of this disconnect has not been examined in detail. Here, we use meteorological reanalysis products, comprehensive climate models, and an idealized atmospheric model to determine the necessary processes for a disconnect between ϕHC and ϕSTJ in the Northern Hemisphere's December–January–February season. We find that a decoupling can occur in a dry general circulation model, indicating that large-scale dynamical processes are sufficient to reproduce the metrics' relationship. It is therefore not reliant on explicit variability in the zonal structure, convection, or radiation. Rather, the disconnect requires a sufficiently realistic climatological basic state. Further, we confirm that the robust disconnect between ϕSTJ and ϕHC across the model hierarchy reveals their differing sensitivities to midlatitude eddy momentum fluxes; ϕHC is consistently coupled to the latitude of maximum eddy momentum flux, but ϕSTJ is not.

Probabilistic skill of statistically downscaled ECMWF S2S forecasts of maximum and minimum temperatures for weeks 1–4 over South Africa

AbstractThe probabilistic forecast skill level of statistically downscaled European Centre for Medium‐Range Weather Forecasts (ECMWF) subseasonal‐to‐seasonal (S2S) forecasts is determined in predicting maximum and minimum temperatures for weeks 1–4 lead times during 20‐year December–January–February (DJF) seasons from 2001 to 2020 over South Africa. Skilful S2S forecasts are vital in assisting decision‐makers in the development of contingency planning for any eventualities that may arise because of weather and climate phenomena. Extreme high‐ and low‐temperature events over a prolonged period can lead to hyperthermia and hypothermia, respectively, and can lead to loss of life. The results from the relative operating characteristic (ROC) and reliability diagrams indicate that the ECMWF S2S model has skill in predicting maximum temperature up to week 3 ahead, particularly over the central and eastern parts of South Africa. The ROC scores indicate that the model has skill in predicting minimum temperature up to week 4 ahead for the above‐normal category, particularly over the central and eastern parts of South Africa. Reliability diagrams indicate that the model has a tendency of overestimating the below‐normal category when predicting both maximum and minimum temperatures for weeks 1–4 lead times over South Africa. Furthermore, canonical correlation analysis (CCA) pattern analysis suggests that when there are anomalously positive and negative predicted 850‐hPa geopotential heights located over South Africa, there are anomalously hot and cold conditions during the DJF seasons over most parts of South Africa, respectively. These results suggests that statistical downscaling of model forecasts can improve forecast skill. Moreover, the results suggest that there is potential for S2S predictions in South Africa, and as such, S2S prediction system for maximum and minimum temperatures can be developed.

The effect of urbanization on temperature indices in the Philippines

AbstractThis paper presents a comprehensive analysis of the effect of urbanization on the surface air temperature (SAT) from 1951 to 2018 in the Philippines. The daily minimum temperature (Tmin) and daily maximum temperature (Tmax) records from 34 meteorological stations were used to derive extreme temperature indices. These stations were then classified as urban or rural based on satellite night‐lights. The results showed a significant difference in the SAT trends between urban and rural stations, indicative of the effect of urbanization in the country. Larger and more significant warming trends were observed in indices related to Tmin than those related to Tmax. In particular, the effects of urbanization were significant in the annual index series of Tmin, diurnal temperature range, minimum Tmin, percentage of days when Tmin was less than the 10th percentile (TN10p), percentage of days when Tmin was greater than 90th percentile (TN90p), and the number of coldest nights. The effects of urbanization were not as clear on the index series of maximum Tmax (TXx), minimum Tmax (TXn), percentage of days when Tmax was less than 10th percentile (TX10p), and the number of hottest days. The effects of urbanization on the annual series of extreme temperature indices were statistically significant at the 95% confidence level, with the exception of Tmax, TXn, TXx, TX10p, and the number of hottest days. Further analysis revealed that the effect of urbanization was the greatest during the DJF (December–January–February) season. These findings serve as a baseline study that focuses on the countrywide effect of urbanization on SAT trends in the Philippines.

Tanzanian rainfall responses to El Niño and positive Indian Ocean Dipole events during 1951–2015

The relative impacts of Indian and Pacific Ocean processes on Tanzanian rainfall was evaluated using composite and correlation analyses. It was found that the seasonal responses of rainfall to positive Indian Ocean Dipole (pIOD) and El Niño events are substantial from September–October–November (SON) to December–January–February (DJF), whereas the Indian Ocean Dipole (IOD) exerts more control than El Niño–Southern Oscillation (ENSO) in both seasons. The associated relationship with the sea surface temperature (SST) and large-scale atmospheric circulations revealed distinct features. For the pure pIOD years, there is above-normal rainfall over the entire country. A strong rainfall condition is evident over the Lake Victoria basin and coastal and northeastern highland parts of the country during SON, while areas of the central and southern highlands exhibit substantial rains during DJF. For the pure El-Niño events, Tanzania has suffered from insignificant, weak, and non-coherent rainfall conditions during SON. However, a contrasting insignificant rainfall signature is found between the northern and southern parts of the country during the subsequent DJF season. For the co-occurrence of pIOD and El Niño, significant, excessive rainfall conditions are restricted to over the northern coast and northeastern areas of the country during SON, consistent with the rainfall pattern for pIOD. A weak, positive rainfall condition is observed over the entire country in the following season of DJF. Generally, in terms of Tanzanian rainfall, the IOD/ENSO variability and the associated impacts can be explained by the anomalous SST and circulation anomalies.

Impact of Large-Scale Climate Indices on Meteorological Drought of Coastal Ghana

The devastating effects of drought on agriculture, water resources, and other socioeconomic activities have severe consequences on food security and water resource management. Understanding the mechanism that drives drought and predicting its variability is important for enhancing early warning and disaster risk management. In this study, meteorological droughts over six coastal synoptic stations were investigated using three-month Standardized Precipitation Index (SPI). The dry seasons of November-December-January (NDJ), December-January-February (DJF), and January-February-March (JFM) were the focal seasons for the study. Trends of dry seasons SPIs were evaluated using seasonal Mann–Kendall test. The relationship between drought SPI and ocean-atmosphere climate indices and their predictive ability were assessed using Pearson correlation and Akaike Information Criterion (AIC) stepwise regression method to select best climate indices at lagged timestep that fit the SPI. The SPI exhibited moderate to severe drought during the dry seasons. Accra exhibited a significant increasing SPI trend in JFM, NDJ, and DJF seasons. Besides, Saltpond during DJF, Tema, and Axim in NDJ season showed significant increasing trend of SPI. In recent years, SPIs in dry seasons are increasing, an indication of weak drought intensity, and the catchment areas are becoming wetter in the traditional dry seasons. Direct (inverse) relationship was established between dry seasons SPIs and Atlantic (equatorial Pacific) ocean's climate indices. The significant climate indices modulating drought SPIs at different time lags are a combination of either Nino 3.4, Nino 4, Nino 3, Nino 1 + 2, TNA, TSA, AMM, or AMO for a given station. The AIC stepwise regression model explained up to 48% of the variance in the drought SPI and indicates Nino 3.4, Nino 4, Nino 3, Nino 1 + 2, TNA, TSA, AMM, and AMO have great potential for seasonal drought prediction over Coastal Ghana.

Understanding the Spatial Organization of Simultaneous Heavy Precipitation Events Over the Conterminous United States

AbstractWe introduce the idea of simultaneous heavy precipitation events (SHPEs) to understand whether extreme precipitation has a spatial organization manifested as specified tracks or contiguous fields with inherent scaling relationships. For this purpose, we created a database of SHPEs using ground‐based precipitation observations recorded by the daily Global Historical Climatology Network across the conterminous United States during 1900–2014. SHPEs are examined for their seasonality, spatial manifestation, orientation, and areal extent. We quantified the spatial distribution of the centroids and principal axes of SHPEs and their quasi‐elliptical manifestations, azimuthal orientations, and areal extents on the ground. Four seasons, December‐January‐February (DJF), March‐April‐May (MAM), June‐July‐August (JJA), and September‐October‐November (SON) are considered to examine the spatial patterns and associated large‐scale atmospheric circulations. Results indicate that there are 54, 58, 103, and 204 SHPEs in DJF, MAM, JJA, and SON seasons, and their longest stretches range on average between 650 and 1,600, between 850 and 1,500, between 950 and 1,550, and between 750 and 1,450 km, respectively. SHPEs in the DJF, MAM, and JJA seasons occur mostly over the Pacific coast and central and midwestern United States, respectively. The atmospheric circulation patterns and mechanisms of precipitable water vapor and moisture transport in the atmosphere are also discussed in relation to these SHPEs. Power laws explain SHPEs' underlying area scaling behavior in all the four seasons, with stronger evidence in DJF and MAM. A seasonal spatial risk model is developed to predict the likelihood of SHPEs. Quantifying the characteristics of SHPEs and modeling their footprints can improve the projections of flood risk and understanding of damages to interconnected infrastructure systems.

Evaluation of Historical CMIP6 Model Simulations of Seasonal Mean Temperature over Pakistan during 1970–2014

This work employed recent model outputs from coupled model intercomparison project phase six to simulate surface mean temperature during the June–July–August (JJA) and December–January–February (DJF) seasons for 1970–2014 over Pakistan. The climatic research unit (CRU TS4.03) dataset was utilized as benchmark data to analyze models’ performance. The JJA season exhibited the highest mean temperature, whilst DJF displayed the lowest mean temperature in the whole study period. The JJA monthly empirical cumulative distribution frequency (ECDF) range (26 to 28 °C) was less than that of DJF (7 to 10 °C) since JJA matched closely to CRU. The JJA and DJF seasons are warming, with higher warming trends in winters than in summers. On temporal scale, models performed better in JJA with overall low bias, low RMSE (root mean square error), and higher positive CC (correlation coefficient) values. DJF performance was undermined with higher bias and RMSE with weak positive correlation estimates. Overall, CanESM5, CESM2, CESM2-WACCM, GFDL-CM4, HadGEM-GC31-LL, MPI-ESM1-2-LR, MPI-ESM1-2-HR, and MRI-ESM-0 performed better for JJA and DJF.

Projected changes in rainfall and temperature over the Philippines from multiple dynamical downscaling models

AbstractTo help meet increasing demands for high‐resolution climate change projections in the Philippines, this study provides the results of multiple dynamically downscaled climate model simulations for projected changes in rainfall and temperature over the country by the mid‐21st century (2036–2065) relative to the baseline period (1971–2000), under the RCP8.5 scenario. The model‐simulated seasonal means of temperature, rainfall, and low‐level wind patterns were first compared with observations during the baseline period. Comparisons made between the model‐derived and APHRODITE observation‐based gridded temperature and rainfall data indicate that the dynamically downscaled simulations provide an overall improvement from their driving global climate models in capturing the spatial patterns of rainfall over the country, and the spatial and temporal characteristics of the country's mean temperature. Future climate projections show that the country's climate is expected to become warmer by the mid‐21st century, with a multi‐model ensemble mean increase of 1.2 to 1.9°C, relative to the baseline period, projected for many parts of the country and across most seasons. Slightly higher increases are projected during the country's hottest season, March–April–May. However, there are large differences in the models' projected rainfall changes by the mid‐21st century across seasons and regions. For most parts of the country, the multi‐model ensemble includes simulations that show increases and simulations that show decreases in rainfall. Nevertheless, there is a tendency of model projections towards wetter conditions over northern and central sections of the country (particularly in the December–January–February season) and drier conditions in the southern region of the country in almost all seasons. The results demonstrate the need for communities in the Philippines to adapt to a future warmer climate and prepare for a range of possible future changes in rainfall and temperature.

Changing aerosol loadings over Central Himalayan region (2007–2016) – A satellite perspective

The paper presents the variation of aerosol columnar variation and vertical profiles over the study area encompassing a portion of Central Himalayan region and the surrounding planar areas. The analysis is carried out using decadal (2007–2016) Level-2 CALIOP (onboard CALIPSO), Level-3 MODIS (onboard Aqua/Terra) and MISR (onboard Terra) datasets. Four seasons have been considered, namely, DJF (December-January-February), MAM (March-April-may), JJA (June-July-August) and SON (September-October-November). The CALIOP data has been screened using quality flags, i.e., CAD score, aerosol layer fraction and extinction quality checks. There are also signatures of elevated aerosol layers, especially during winter (DJF) and post-monsoon (SON) seasons. Latitudinal and longitudinal variations of the vertical structure have also been presented. The HYSPLIT back-trajectory analysis has been carried out in order to explore the changes in the sources of the aerosols during different seasons. It is seen that as we go higher up in the altitudes, the long range transport becomes more dominant. Compared to the other seasons, the air masses are more localized in JJA season. The changing aerosol amounts at different heights of the atmosphere have also been discussed in terms of decadal trends for the four seasons. These trends have been supplemented by the AOD trends from the passive satellite sensors. We find that aerosol amounts have increased in the last decade during DJF and SON seasons, when the atmospheric conditions are more stable. On the contrary, aerosol amounts have significantly decreased in JJA season throughout the vertical column. The effect of changing amounts of aerosols may have impacts on the snow cover variability as well. In this connection, the seasonal trends in snow cover have also been explored during the considered decadal period. Especially during the DJF and SON seasons, decreasing trends in snow cover are observed when the aerosol amounts have been found to be increased.

Effects of climate change on water savings and water security from rainwater harvesting systems

Climate change presents major uncertainties in future safe water access especially in rural communities that are already faced with water insecurity. For Uganda, rainwater harvesting has become a major adaptation strategy to ensure the attainment of safe water access for all. However, climate change effects on rainwater harvesting potential remains largely unexplored. Therefore, this paper assesses climate change effects on water savings and water security from rainwater harvesting systems for Kabarole district, Uganda. The top performing Global circulation models (GCMs), used in this study included; MIROC5, BCC-CSM-1-M, CNRM-CM5, ACCESS1-0, HADGEM2-ES and HADGEM2-CC using projections for 2025–2055 (2040s) and 2060–2090 (2070s) periods. Seasonal analysis used the 4 seasons for Uganda, namely; the 2 dry seasons of December, January, February (DJF) and June, July, August (JJA) and the 2 rainy seasons of September, October, November (SON) and March, April, May (MAM). Although there are conflicting model predictions, generally, the models agree that water savings and security will reduce in DJF and MAM and increase in JJA and SON seasons. MAM rain season will be most affected with water savings and security projected to reduce by more than 50% in the 2070s period. Therefore, households should harness the increased water savings in JJA and SON to cater for the predicted reduction in water savings in MAM and DJF seasons especially for the 2070s period. A larger tank of 5 m3 along with at least 50 m2 roof size combined with a low cost household water treatment is thus recommended.

Effect of Climate Change on Reliability of Rainwater Harvesting Systems for Kabarole District, Uganda

This paper assesses the effect of climate change on reliability of rainwater harvesting systems for Kabarole district, Uganda, as predicted by 6 best performing global circulation models (GCMs). A daily water balance model was used to simulate the performance of a rainwater harvesting system using historical daily rainfall data for 20 years. The GCMs used to generate daily rainfall projections for 2025–2055 and 2060–2090 periods included; ACCESS1-0, BCC-CSM-1-M, CNRM-CM5, HADGEM2-CC, HADGEM2-ES and MIROC5. Analysis was based on the Ugandan weather seasons which included March, April, May (MAM) and September, October, November (SON) rain seasons in addition to December, January, February (DJF) and June, July, August (JJA) dry seasons. While an increase in reliability is predicted for the SON season, the worst-case scenario is projected during the MAM season with a reliability reduction of over 40% for the 2055–2090 period. This corresponds to a 27% reduction in water security for the same period. The DJF season is also expected to experience reduced water security by 1–8% for 2025–2055 and 2060–2090 with a 0.5 m3 tank size. Therefore, some form of extra harvesting surface and increased tank size will be required to maintain 80% systems reliability considering climate change.

Impact of different types of ENSO conditions on seasonal precipitation and streamflow in the Southeastern United States

ABSTRACTRecent advancement in climate sciences has discovered two different types of El Niño events, namely the eastern Pacific El Niño (EP‐El Niño) and central Pacific El Niño (CP‐El Niño). Identifying how watershed responses to different El Niño‐Southern Oscillation (ENSO) events, including both El Niño and La Niña conditions, is critical in understanding how the oceanic teleconnections possibly affect local precipitation and streamflow. In this study, 70 watersheds with minimum human impact during the period 1950–2010 in the Southeastern United States (SEUS) were selected to examine whether streamflow exhibits different patterns in EP‐ and CP‐El Niño conditions, and La Niña events. Similar analyses were applied to 187 precipitation stations in SEUS. Analyses reveal that for the peak season of both EP‐El Niño and CP‐El Niño years, December–January–February (DJF), seasonal average streamflow and precipitation is higher than long‐term average. CP‐El Niño, however, weakens the wetting effect brought by traditional EP‐El Niño, leading to smaller percentage of increases in seasonal precipitation/streamflow for most stations in the SEUS. In the decaying season March–April–May (MAM) of ENSO signals, streamflow for both EP‐ and CP‐El Niño is higher than its corresponding climatology. The increased amplitude is even greater in MAM compared to that of the DJF season. During La Niña events, reduced precipitation and streamflow are observed for large portion of stations in SEUS, especially for the DJF and MAM season. The use of ENSO in water resources management is often limited to boreal winter season for the SEUS, as its signal is well reflected in hydrological responses. This study finds that for the June–July–August (JJA) season followed by the decay of ENSO signals in MAM, there is striking difference in the magnitude of precipitation and streamflow for EP‐El Niño and CP‐El Niño events.

A new integrated and homogenized global monthly land surface air temperature dataset for the period since 1900

A new dataset of integrated and homogenized monthly surface air temperature over global land for the period since 1900 [China Meteorological Administration global Land Surface Air Temperature (CMA-LSAT)] is developed. In total, 14 sources have been collected and integrated into the newly developed dataset, including three global (CRUTEM4, GHCN, and BEST), three regional and eight national sources. Duplicate stations are identified, and those with the higher priority are chosen or spliced. Then, a consistency test and a climate outlier test are conducted to ensure that each station series is quality controlled. Next, two steps are adopted to assure the homogeneity of the station series: (1) homogenized station series in existing national datasets (by National Meteorological Services) are directly integrated into the dataset without any changes (50% of all stations), and (2) the inhomogeneities are detected and adjusted for in the remaining data series using a penalized maximal t test (50% of all stations). Based on the dataset, we re-assess the temperature changes in global and regional areas compared with GHCN-V3 and CRUTEM4, as well as the temperature changes during the three periods of 1900–2014, 1979–2014 and 1998–2014. The best estimates of warming trends and there 95% confidence ranges for 1900–2014 are approximately 0.102 ± 0.006 °C/decade for the whole year, and 0.104 ± 0.009, 0.112 ± 0.007, 0.090 ± 0.006, and 0.092 ± 0.007 °C/decade for the DJF (December, January, February), MAM, JJA, and SON seasons, respectively. MAM saw the most significant warming trend in both 1900–2014 and 1979–2014. For an even shorter and more recent period (1998–2014), MAM, JJA and SON show similar warming trends, while DJF shows opposite trends. The results show that the ability of CMA-LAST for describing the global temperature changes is similar with other existing products, while there are some differences when describing regional temperature changes.

Separation of the lapse rate and the cold point tropopauses in the tropics and the resulting impact on cloud top-tropopause relationships

Four years of temperature profiles from Constellation Observing System for Meteorology, Ionosphere, and Climate GPS satellite retrievals are used to examine the difference between the World Meteorological Organization lapse rate definition and the cold point definition of the tropopause in the tropics. The separation between the cold point tropopause (CPT) and lapse rate tropopause (LRT) heights is quantified in seasonal averages and with the frequency of occurrence. In seasonal averages, small separations, <0.5 km, are found in the deep tropics, increasing to ~1 km toward higher latitudes and maximizing at ~1.5 km near the jet streams. The seasonal average separations show significant longitudinal structures in the December-January-February (DJF) and June-July-August (JJA) seasons. Case studies indicate that breaking Rossby waves and their effects extending into the equatorial region are responsible for the longitudinal structure in the DJF season. The seasonal average CPT-LRT separation therefore identifies the regions of the tropical upper troposphere-lower stratosphere that are controlled by extratropical forcing. Examination of individual profiles shows that a small yet significant fraction (~12%) of temperature profiles has CPT-LRT separations of 1 km or larger in the region of small seasonal average separation. These large separations are produced by wave perturbations of the upper tropospheric temperature structure. The impact of tropopause separation on the cloud top-tropopause relationship is examined using colocated CALIPSO cloud top data. We find that the frequency of clouds above the tropopause is reduced by approximately 50% if the CPT is used instead of LRT. The occurrence of clouds above the CPT is nevertheless significant, especially over the western Pacific in the DJF season and over the Asian monsoon region in the JJA season.

Summer precipitation variability over Southeastern South America in a global warming scenario

December–January–February (DJF) rainfall variability in southeastern South America (SESA) is studied in 18 coupled general circulation models from the WCRP/CMIP3 dataset, for present climate and the SRES-A1B climate change scenario. The analysis is made in terms of properties of the first leading pattern of rainfall variability in the region, characterized by a dipole-like structure with centers of action in the SESA and South Atlantic Convergence Zone (SACZ) regions. The study was performed to address two issues: how rainfall variability in SESA would change in a future climate and how much of that change explains the projected increasing trends in the summer mean rainfall in SESA identified in previous works. Positive (negative) dipole events were identified as those DJF seasons with above (below) normal rainfall in SESA and below (above) normal rainfall in the SACZ region. Results obtained from the multi-model ensemble confirm that future rainfall variability in SESA has a strong projection on the changes of seasonal dipole pattern activity, associated with an increase of the frequency of the positive phase. In addition, the frequency increase of positive dipole phase in the twenty first century seems to be associated with an increase of both frequency and intensity of positive SST anomalies in the equatorial Pacific, and with a Rossby wave train-like anomaly pattern linking that ocean basin to South America, which regionally induces favorable conditions for moisture transport convergence and rainfall increase in SESA.

ENSO induced monthly oscillations of precipitation: the unique case of the south tropical Indian Ocean in austral summer

Seasonal (three-month average) climate forecasts have advanced due in large part to improved modeling of the ENSO phenomenon. Long-range monthly forecasts are more problematic because of internal atmospheric variability. Further, it is often assumed that monthly precipitation anomalies are representative of the overall seasonal anomaly. This is not always the case as, according to the Global Precipitation Climatology Project Version 2.1 data set, up to 20% of areas demonstrating some significant teleconnection to ENSO show El Nino minus La Nina differences of one sign in the middle month and the opposite sign in the adjacent months. Most interestingly, this maximum percentage occurs in December–January–February (DJF), a time when the ENSO boundary forcing is strongest. These oscillatory DJF seasons also cluster in space—with significant positive–negative-positive differences in the western South Tropical Indian Ocean (STIO) and negative–positive–negative differences in the far eastern STIO. Representative gauges confirm that these precipitation patterns have been associated with ENSO events since 1951, and pentad precipitation data confirm that they are confined to DJF and evolve at the monthly scale. The abrupt end of the Indian Ocean Dipole mode in January, an increase in the importance of local SST anomalies in February, and an ENSO-induced mid-latitude Rossby wave during austral summer combine to generate the cross-basin precipitation gradient around 15°S.

Uncertainty of Future Precipitation Change Due to Global Warming Associated with Sea Surface Temperature Change in the Tropical Pacific

The effects of future tropical Pacific sea surface temperature (SST) changes on regional precipitation projections are statistically studied for December–January–February (DJF) and June–July–August (JJA) in the Coupled Model Intercomparison Project phase 3 (CMIP3) experiments. The present climate precipitation responses to Nino3 SST variability appear as an uncertainty with regards to future regional precipitation changes among the CMIP3 model projections. Compared with the CMIP3 models projecting La Nina-like Pacific SST changes, the models projecting El Nino-like Pacific SST changes tend to simulate more precipitation in future DJF over the tropical central Pacific, southeastern North America and the tropical western Indian Ocean, and less over the tropical northwestern Pacific, the tropical South Pacific and tropical South America. For JJA, the models projecting El Nino-like Pacific SST changes tend to simulate greater future precipitation in the tropical central Pacific and less over the Maritime Continent and around Central America. Interestingly, the present climate features of the delayed JJA precipitation response to previous DJF Nino3 SST variability also appear as differences in future JJA precipitation changes between the models projecting future El Nino-like and La Nina-like Pacific SST changes in DJF. Compared to the later models, the former models have a tendency to show more precipitation south of Japan and south of the equator in the central to eastern Pacific, and less in the subtropical northwestern Pacific. CMIP3 model analysis indicates that the projected El Nino-like SST changes are related to the present precipitation climatology of the models in the near-equatorial eastern Pacific for each DJF and JJA season, suggesting the importance of realistically simulating present precipitation climatology in the tropical Pacific for future projections.

Prediction of the Madden–Julian oscillation with the POAMA dynamical prediction system

Predictions of the Madden–Julian oscillation (MJO) are assessed using a 10-member ensemble of hindcasts from POAMA, the Australian Bureau of Meteorology coupled ocean–atmosphere seasonal prediction system. The ensemble of hindcasts was initialised from observed atmosphere and ocean initial conditions on the first of each month during 1980–2006. 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–January–February season and minimum skill in June–July–August. 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.

The Teleconnection between the Western Indian and the Western Pacific Oceans

Abstract Shown in this study are a teleconnection pattern relating outgoing longwave radiation (OLR) anomalies over the western Pacific Ocean and sea surface temperature anomalies (SSTAs) over the western Indian Ocean over two seasons [September–October–November (SON) and December–January–February (DJF)] at zero lag from observations and atmospheric general circulation model (AGCM) integrations. This teleconnection pattern suggests that a positive SSTA in the SON and DJF seasons over the western Indian Ocean increases the contemporaneous positive OLR anomalies over the western Pacific Ocean. This teleconnection pattern is also simulated by the Center for Ocean–Land–Atmosphere studies (COLA) AGCM forced with observed SST. From the experimental COLA AGCM runs (wherein the Pacific Ocean SST variability is suppressed except for the climatological annual cycle), it is diagnosed that the interannual variability of OLR over the western Pacific Ocean persists because of this teleconnection. This teleconnection pa...

A simple approach for combining seasonal forecasts for southern Africa

AbstractCurrently the South African Weather Service (SAWS) compiles seasonal rainfall and temperature outlooks by combining output from various models in a subjective manner. A group of scientists discusses the forecasts from a variety of models and then decides, from these discussions, what the strongest climate signal is. A map is drawn to represent the ‘gut feeling’ from the specialists based on the results from the various models. Although the models used are all scientifically sound, the compilation of the outlook remains subjective. In order to compile a single probability forecast out of the many different model outputs an objective method has to be implemented. This paper proposes such a method. The results were tested during the December–January–February season over a retroactive period from 1991/92 to 1999/2000. Results show that a combination of different models consistently delivers a more skilful forecast than any individual model on its own. Copyright © 2003 Royal Meteorological Society.