Recent and future sea surface temperature trends in tropical pacific warm pool and cold tongue regions

Using coral data, sea surface temperature (SST) reanalysis data, and Climate Model Intercomparison Project III (CMIP3) data, we analyze 20th-century and future warm pool and cold tongue SST trends. For the last 100?years, a broad La Nina-like SST trend, in which the warming trend of the warm pool SST is greater than that of the cold tongue SST, has appeared in reanalysis SST data sets, 20C scenario experiments of the CMIP3 data and less significantly in coral records. However, most Coupled General Circulation Models subjected to scenarios of future high greenhouse gas concentrations produce larger SST warming trends in cold tongues than in warm pools, resembling El Nino-like SST patterns. In other words, warmer tropical climate conditions correspond to stronger El Nino-like response. Heat budget analyses further verify that warmer tropical climates diminish the role of the ocean’s dynamic thermostat, which currently regulates cold tongue temperatures. Therefore, the thermodynamic thermostat, whose efficiency depends on the mean temperature, becomes the main regulator (particularly via evaporative cooling) of both warm pool and cold tongue temperatures in future warm climate conditions. Thus, the warming tendency of the cold tongue SST may lead that of the warm pool SST in near future.     

Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004)

We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework.     

The Asian Dust Aerosol Model 2 (ADAM2) with the use of Normalized Difference Vegetation Index (NDVI) obtained from the Spot4/vegetation data

The operational Asian Dust Aerosol Model (ADAM)1 in Korea Meteorological Administration has been modified to the ADAM2 model to be used as an operational forecasting model all year round not only in Korea but also in the whole Asian domain (70-160°E and 5-60°N) using the routinely available World Meteorological Organization (WMO) surface reporting data and the Spot/vegetation Normalized Difference Vegetation Index (NDVI) data for the period of 9 years from 1998 to 2006. The 3-hourly reporting WMO surface data in the Asian domain have been used to re-delineate the Asian dust source region and to determine the temporal variation of the threshold wind speed for the dust rise. The dust emission reduction factor due to vegetation in different surface soil-type regions (Gobi, sand, loess, and mixed soil) has been determined with the use of NDVI data. It is found that the threshold wind speed for the dust rise varies significantly with time (minimum in summer and maximum in winter) and surface soil types with the highest threshold wind speed of 8.0 m?s^?1 in the Gobi region and the lowest value of 6.0 m?s^?1 in the loess region. The statistical analysis of the spot/vegetation NDVI data enables to determine the emission reduction factor due to vegetation with the free NDVI value that is the NDVI value without the effect of vegetation and the upper limit value of NDVI for the dust rise in different soil-type regions. The modified ADAM2 model has been implemented to simulate two Asian dust events observed in Korea for the periods from 31 March to 2 April 2007 (a spring dust event) and from 29 to 31 December 2007 (a winter dust event) when the observed PM₁₀ concentration at some monitoring sites in the source region exceeds 9,000 μg m^?3. It is found that ADAM2 model successfully simulates the observed high dust concentrations of more than 8,000 μg m^?3 in the dust source region and 600 μg m^?3 in the downstream region of Korea. This suggests that ADAM2 has a great potential for the use of an operational Asian dust forecast model in the Asian domain.     

Spatial drought reconstructions for central High Asia based on tree rings

Spatial reconstructions of drought for central High Asia based on a tree-ring network are presented. Drought patterns for central High Asia are classified into western and eastern modes of variability. Tree-ring based reconstructions of the Palmer drought severity index (PDSI) are presented for both the western central High Asia drought mode (1587–2005), and for the eastern central High Asia mode (1660–2005). Both reconstructions, generated using a principal component regression method, show an increased variability in recent decades. The wettest epoch for both reconstructions occurred from the 1940s to the 1950s. The most extreme reconstructed drought for western central High Asia was from the 1640s to the 1650s, coinciding with the collapse of the Chinese Ming Dynasty. The eastern central High Asia reconstruction has shown a distinct tendency towards drier conditions since the 1980s. Our spatial reconstructions agree well with previous reconstructions that fall within each mode, while there is no significant correlation between the two spatial reconstructions.     

Dynamics of sea-surface temperature anomalies in the Southern Ocean diagnosed from a 2D mixed-layer model

We analyze the processes responsible for the generation and evolution of sea-surface temperature anomalies observed in the Southern Ocean during a decade based on a 2D diagnostic mixed-layer model in which geostrophic advection is prescribed from altimetry. Anomalous air–sea heat flux is the dominant term of the heat budget over most of the domain, while anomalous Ekman heat fluxes account for 20–40% of the variance in the latitude band 40°?60°S. In the ACC pathway, lateral fluxes of heat associated with anomalous geostrophic currents are a major contributor, dominating downstream of several topographic features, reflecting the influence of eddies and frontal migrations. A significant fraction of the variability of large-scale SST anomalies is correlated with either ENSO or the SAM, each mode contributing roughly equally. The relation between the heat budget terms and these climate modes is investigated, showing in particular that anomalous Ekman and air–sea heat fluxes have a co-operating effect (with regional exceptions), hence the large SST response associated with each mode. It is further shown that ENSO- or SAM-locked anomalous geostrophic currents generate substantial heat fluxes in all three basins with magnitude comparable with that of atmospheric forcings for ENSO, and smaller for the SAM except for limited areas. ENSO-locked forcings generate SST anomalies along the ACC pathway, and advection by mean flows is found to be a non-negligible contribution to the heat budget, exhibiting a wavenumber two zonal structure, characteristic of the Antarctic Circumpolar Wave. By contrast SAM-related forcings are predominantly zonally uniform along the ACC, hence smaller zonal SST gradients and a lesser role of mean advection, except in the SouthWest Atlantic. While modeled SST anomalies are significantly correlated with observations over most of the Southern Ocean, the analysis of the data-model discrepancies suggests that vertical ocean physics may play a significant role in the nonseasonal heat budget, especially in some key regions for mode water formation.     

Climate change,weather variability and corn yield at a higher latitude locale: Southwestern Quebec

Climate change has led to increased temperatures, and simulation models suggest that this should affect crop production in important agricultural regions of the world. Nations at higher latitudes, such as Canada, will be most affected. We studied the relationship between climate variability (temperature and precipitation) and corn yield trends over a period of 33 years for the Monteregie region of south-western Quebec using historical yield and climate records and statistical models. Growing season mean temperature has increased in Monterregie, mainly due to increased September temperature. Precipitation did not show any clear trend over the 33 year period. Yield increased about 118 kg ha⁻¹ year⁻¹ from 1973 to 2005 (under normal weather conditions) due mainly to changes in technology (genetics and management). Two climate variables were strongly associated with corn yield variability: July temperature and May precipitation. These two variables explain more than a half of yield variability associated with climate. In conclusion, July temperatures below normal and May precipitation above normal have negative effects on corn yield, and the growing seasons have warmed, largely due to increases in the September temperature.     

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.     

Between structure and agency: Livelihoods and adaptation in Ghana's Central Region

This article examines adaptation decision-making through a diversified livelihoods strategy that distributes risk across market and subsistence production in Ghana's Central Region. Specifically, it asks how this strategy, which is an adaptation to a relatively recent convergence of economic and environmental uncertainty in this context, is accepted and reproduced by society at large, even as this adaptation results in unevenly distributed benefits and costs. An examination of the case in question suggests that the persistence of this adaptation has little to do with its material outcomes. This adaptation persists because, despite its unequal and less-than-optimal material outcomes, it is rooted in the ability of men to link this adaptation to existing gender roles, thereby legitimizing the adaptation and the gendered roles it relies upon. This finding calls into question the very idea of a successful adaptation, and suggests that much more attention must be paid to the persistence of particular adaptations if we are to understand existing adaptations and build upon them to enhance local capacities for managing economic and environmental change.     

Observable consequences of kinetic and thermal AGN feedback in elliptical galaxies and galaxy clusters

We have constructed an analytical model of active galactic nuclei (AGN) feedback and studied its implications for elliptical galaxies and galaxy clusters. The results show that momentum injection above a critical value will eject material from low-mass elliptical galaxies, and leads to an X-ray luminosity, L _X, that is  ∝σ⁸⁻¹⁰  , depending on the AGN fuelling mechanism, where σ is the velocity dispersion of the hot gas. This result agrees well with both observations and semi-analytic models. In more massive ellipticals and clusters, AGN outflows quickly become buoyancy dominated. This necessarily means that heating by a central cluster AGN redistributes the intracluster medium (ICM) such that the mass of hot gas, within the cooling radius, should be  ∝ L _X(< r _cool)/[ g ( r _cool)σ]  , where   g ( r _cool)  is the gravitational acceleration at the cooling radius. This prediction is confirmed using observations of seven clusters. The same mechanism also defines a critical ICM cooling time of  ∼0.5 Gyr  , which is in reasonable agreement with recent observations showing that star formation and AGN activity are triggered below a universal cooling time threshold.