The reluctance of resource-users to adopt seasonal climate forecasts to enhance resilience to climate variability on the rangelands

As the impacts of climate-change on resource-dependent industries manifest, there is a commensurate effort to identify and implement strategies to reduce them. Yet, even when useful knowledge and tools exist, there can be poor adoption of adaptation strategies. We examine the reasons behind sub-optimal adoption of seasonal climate forecasts by graziers for managing climate variability. We surveyed 100 graziers in north-east Queensland, Australia and examined the influence of adaptive capacity, resource-dependency and forecast-perception on uptake. Technical perceptions were not important. Strategic skills, environmental awareness and social capital were. Results suggest that social factors (but not technical factors) are significant. These insights are important for adaptation planning and for maximising the resilience of communities and industries dependent on climate-sensitive resources.     

Simulation capability of tropical and extratropical seasonal climate anomalies over South America

An ensemble of 20 extended integrations of the atmospheric model CSIRO Mark 2, forced with the sea-surface temperature observed during the 1986–1998 period, was performed to analyze the simulation capability of seasonal climate anomalies over South America and adjacent oceanic areas. Variations of the simulation skill within the region and during the experimental period were assessed through standard statistical measures and compared to the signal-to-noise ratio distribution. Before the skill assessment, model systematic errors were thoroughly evaluated. The results confirm that the simulation skill is very high in tropical oceanic areas, and decreases rapidly towards middle and high latitudes. Model performance at mid and high atmospheric levels is substantially better than at low levels. Relatively high simulation capability was found over the Pacific Ocean between the equator and the Antarctic coast, which is coherent with the presence of three relative maximums in the signal-to-noise ratio, similar to the increase of the forced variance found by several authors over much of the Pacific–North American pattern region. Rainfall rate and second-order moments associated with the cyclonic activity and the meridional eddy fluxes of heat and humidity are better simulated in a narrow strip parallel to the SPCZ and extending further southeast into mid latitudes of the continent. The simulation skill noticeably improves during the warm and cold ENSO phases, in correspondence with an intensification of the signal-to-noise ratio, and useful rainfall anomaly simulations can be obtained over the Amazonas and Rio de la Plata river basins.     

Sensitivity of hurricane intensity forecasts to physical initialization

Summary Intensity forecasts of a hurricane are shown to be quite sensitive to the initial meso-convective scale precipitation distributions. These are included within the data assimilation using a physical initialization that was developed at Florida State University. We show a case study of a hurricane forecast where the inclusion of the observed precipitation did provide reasonable intensity forecasts. Further experimentation with the inclusion or exclusion of individual meso-convective rainfall elements, around and over the storm, shows that the intensity forecasts were quite sensitive to these initial rainfall distributions. The exclusion of initial rain in the inner rain area of a hurricane leads to a much reduced intensity forecast, whereas that impact is less if the rainfall of an outer rain band was initially excluded.Intensity forecasts of hurricanes may be sensitive to a number of factors such as sea surface temperature anomalies, presence or absence of concentric eye walls, potential vorticity interactions in the upper troposphere and other environmental factors.This paper is a sequel to a recent study, Krishnamurti et al., 1997, on the prediction of hurricane OPAL of 1995 that was a category III storm over the Gulf of Mexico. In that study we showed successful forecasts of the storm intensity from the inclusion of observed rainfall distributions within physical initialization. In that paper we examined the issues of diabatic potential vorticity and the angular momentum in order to diagnose the storm intensity. All of the terms of the complete Ertel potential vorticity equation were evaluated and it was concluded that the diabatic contributions to the potential vorticity were quite important for the diagnosis of the storm's intensity. The present paper addresses some sensitivity issues related to the individual mesoconvective precipitating elements.With 4 Figures     

Improved seasonal climate forecasts for the Caribbean region using the Florida State University Synthetic Superensemble

Summary Climate variations in the Caribbean, largely manifest in rainfall activity, have important consequences for the large-scale water budget, natural vegetation, and land use in the region. The wet and dry seasons will be defined, and the important roles played by the El Ni?o-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) in modulating the rainfall during these seasons will be discussed. The seasonal climate forecasts in this paper are made by 13 state of the art coupled atmosphere-ocean general circulation models (CGCMs) and by the Florida State University Synthetic Superensemble (FSUSSE), whose forecasts are obtained by a weighted combination of the individual CGCM forecasts based on a training period. The success of the models in simulating the observed 1989–2001 climatology of the various forecast parameters will be examined and linked to the models’ success in predicting the seasonal climate for individual years. Seasonal forecasts will be examined for precipitation, sea-surface temperature (SST), 2-meter air temperature, and 850 hPa u- and v-wind components during the period 1989–2001. Evaluation metrics include root mean square (RMS) error and Brier skill score. It will be shown that the FSUSSE is superior to the individual CGCMs and their ensemble mean both in simulating the 1989–2001 climatology for the various parameters and in predicting the seasonal climate of the various parameters for individual years. The seasonal climate forecasts of the FSUSSE and of the ensemble mean of the 13 state of the art CGCMs will be evaluated for years (during the period 1989–2001) that have particular ENSO and NAO signals that are known to influence Caribbean weather, particularly the rainfall. It will be shown that the FSUSSE provides superior forecasts of rainfall, SST, 2-meter air temperature, and 850 hPa u- and v-wind components during dry summers that are modulated by negative SOI and/or positive NAO indices. Such summers have become a feature of a twenty-year pattern of drought in the Caribbean region. The results presented in this paper will show that the FSUSSE is a valuable tool for forecasting rainfall and other atmospheric and oceanic variables during such periods of drought.     

Impacts of SST and SST Anomalies on Low-Frequency Oscillation in the Tropical Atmosphere

Considering the multiscale character of LFO effects of SST on LFO in the tropical atmosphere （low-frequency oscillation） in the tropical atmosphere, the are discussed by using an absolute ageostrophic, baroclinic model. Here, SST effects include sea surface heating and forcing of SST anomalies （SSTAs）. Studies of the influences of sea surface heating on LFO frequency and stability show that sea surface heating can slow the speed of waves and lower their frequency when SST is comparatively low; while higher SST leads to unstable waves and less periods of LFO. Since the impact of a SSTA on ultra-long waves is more evident than that on kilometer-scale waves, long-wave approximation is used when we continue to study the effect of SSTAs. Results indicate that SSTAs can lead to a longer period of LFO, and make waves unstable. In other words, positive （negative） SSTAs can make waves decay （grow）.     

Predictability of the western North Pacific summer climate associated with different ENSO phases by ENSEMBLES multi-model seasonal forecasts

Predictability of the western North Pacific (WNP) summer climate associated with different El Niño–Southern Oscillation (ENSO) phases is investigated in this study based on the 1-month lead retrospective forecasts of five state-of-the-art coupled models from ENSEMBLES. During the period from 1960 to 2005, the models well capture the WNP summer climate anomalies during most of years in different ENSO phases except the La Niña decaying summers. In the El Niño developing, El Niño decaying and La Niña developing summers, the prediction skills are high for the WNP summer monsoon index (WNPMI), with the prediction correlation larger than 0.7. The high prediction skills of the lower-tropospheric circulation during these phases are found mainly over the tropical western Pacific Ocean, South China Sea and subtropical WNP. These good predictions correspond well to their close teleconnection with ENSO and the high prediction skills of tropical SSTs. By contrast, for the La Niña decaying summers, the prediction skills are considerably low with the prediction correlation for the WNPMI near to zero and low prediction skills around the Philippines and subtropical WNP. These poor predictions relate to the weak summer anomalies of the WNPMI during the La Niña decaying years and no significant connections between the WNP lower-tropospheric circulation anomalies and the SSTs over the tropical central and eastern Pacific Ocean in observations. However, the models tend to predict an apparent anomalous cyclone over the WNP during the La Niña decaying years, indicating a linearity of the circulation response over WNP in the models prediction in comparison with that during the El Niño decaying years which differs from observations. In addition, the models show considerable capability in describing the WNP summer anomalies during the ENSO neutral summers. These anomalies are related to the positive feedback between the WNP lower-tropospheric circulation and the local SSTs. The models can capture this positive feedback but with some uncertainties from different ensemble members during the ENSO neutral summers.     

Sensitivity of medium-range weather forecasts to the use of reference atmosphere

In this paper, the authors develop the earlier work of Chen Jiabin et al. (1986). In order to reduce spectral truncation errors, the reference atmosphere has been introduced in ECMWF model, and the spectrally-represented variables, temperature, geopotential height and orography, are replaced by their deviations from the reference atmos-phere. Two modified semi- implicit schemes have been proposed to alleviate the computational instability due to the introduction of reference atmosphere. Concerning the deviation of surface geopotential height from reference atmos-phere, an exact computational formulation has been used instead of the approximate one in the earlier work. To re-duce aliasing errors in the computations of the deviation of the surface geopotential height, a spectral fit has been used slightly to modify the original Gaussian grid-point values of orography.A series of experiments has been performed in order to assess the impact of the reference atmosphere on ECMWF medium- range forecasts at the resolution T21, T42 and T63, The results we have obtained reveal that the reference atmosphere introduced in ECMWF spectral model is generally beneficial to the mean statistical scores of 1000-200 hPa height 10-day forecasts over the globe. In the Southern Hemisphere, it is a clear improvement for T21, T42 and T63 throughout the 10-day forecast period, In the Northern Hemisphere, the impact of the reference atmos-phere on anomaly correlation is positive for resolution T21, a very slightly damaging at T42 and almost neutral at T63 in the range of day 1 to day 4. Beyond the day 4 there is a clear improvement at all resolutions.     

土壤湿度初始异常对东亚区域气候模拟影响的敏感性试验

利用耦合了CLM3.5陆面模式的区域气候模式RegCM4.0,通过敏感性试验,探讨了人为减小春季初始土壤体积水含量对短期时间尺度东亚夏季气候模拟的可能影响。结果表明:较低的初始土壤湿度场能够明显改变区域的地表能量平衡,引起地表净长波辐射和感热通量的显著增加,进而加强了地表对大气的加热,因而引起东亚大范围地区特别是中国东部、印度北部和中亚地区地表温度、气温的升高。与气温不同,初始土壤湿度场对降水的影响很小而且有较大的不确定性,同时偏暖的下垫面使得对流层中高层出现暖高压异常,但这些影响均不显著。综合来看,土壤湿度初始场的初始异常,对RegCM4.0 模式东亚气候模拟的结果有一定影响,特别是在地表温度、气温和能量平衡方面,应在以后的模拟中加以考虑。     

Atmospheric anomalies related to interdecadal variability of SST in the North Pacific

Anomalous patterns of the atmospheric circulation and climate are studied corresponding to the two basic interdecadal variation modes of sea surface temperature (SST) in the North Pacific, namely, the 25-35-year mode and the 7-10-year mode. Results clearly indicate that corresponding to the positive and negative phases of the interdecadal modes of SST anomaly (SSTA) in the North Pacific, the anomalous patterns of the atmospheric circulation and climate are approximately out of phase, fully illustrating the important role of the interdecadal modes of SST. Since the two interdecadal modes of SSTA in the North Pacific have similar horizontal structures, their impacts on the atmospheric circulation and climate are also analogous. The impact of the interdecadal modes of the North Pacific SST on the atmospheric circulation is barotropic at middle latitudes and baroclinic in tropical regions.     

Participatory modeling in dairy farm systems: a method for building consensual environmental sustainability using seasonal climate forecasts

Dairy farmers face increasing pressure to decrease environmental impact while remaining economically viable. Adaptation of farm management practices in response to seasonal climate forecasts may be one means of achieving these objectives. This paper describes the interactive and iterative process by which farmers, researchers, extension agents, regulatory agencies, and other stakeholders collaborated to create, calibrate, and validate the Dynamic North Florida Dairy Farm model (DyNoFlo), a whole-farm decision support system to decrease nitrogen leaching while maintaining profitability under variable climate conditions. Participatory modeling may enhance the creation of adoptable and adaptable user-friendly models that include environmental, economic and biophysical components. By providing farmers, policy makers, and other stakeholders with a more holistic view of current practices, common ground among them was more easily identified and collaboration was fostered. Farmer values included willingness to be good environmental stewards when they are profitable. The participatory research and development process enhanced understanding of and potential adaptation to seasonal climate variability conditioned to the El Niño Southern Oscillation (ENSO) phases in light of increasing environmental regulations and economic challenges. Adoption of the collaboratively-developed DyNoFlo is expected to be higher than usual because stakeholders feel greater ownership of the final product.     

Spatial reorganization of SST anomalies by stationary atmospheric waves

The dynamics of sea surface temperature (SST) anomalies that force stationary atmospheric waves, which in turn, feed back on the SST field is addressed. The phenomena is isolated by analyzing the dynamics of a slab ocean that is thermally coupled to an atmospheric model. Particular emphasis is put on identifying SST structures that are weakly damped by the joint effect of air–sea heat transfer and atmospheric wave dynamics.A frame work is presented that singles out long-lived SST features in a slab ocean coupled to an arbitrary linear atmospheric model. It is demonstrated that an SST anomaly eventually disintegrates into a number of propagating wave packets. The wave packets are confined in a Gaussian envelope, and each packet is tied to a stationary wave of a particular wavelength. These structures are a manifestation of coupled SST-atmosphere mode, for which the atmosphere and the ocean nearly are in thermal equilibrium. However, a small disequilibrium causes the wave packet to propagate and to broaden in an apparent diffusive manner.Central ideas pertaining to the mid-latitude SST dynamics are illustrated by analyzing the thermal feedback between a two-level atmospheric model (on a β-plane) and a dynamically passive slab ocean. The relevance of the present idealized coupled-modes to the SST variability in the mid-latitudes and in atmospheric GCMs coupled to slab oceans is discussed.     

An analysis of seasonal predictability in coupled model forecasts

In the recent decade, operational seasonal prediction systems based on initialized coupled models have been developed. An analysis of how the predictability of seasonal means in the initialized coupled predictions evolves with lead-time is presented. Because of the short lead-time, such an analysis for the temporal behavior of seasonal predictability involves a mix of both the predictability of the first and the second kind. The analysis focuses on the lead-time dependence of ensemble mean variance, and the forecast spread. Further, the analysis is for a fixed target season of December?CJanuary?CFebruary, and is for sea surface temperature, rainfall, and 200-mb height. The analysis is based on a large set of hindcasts from an initialized coupled seasonal prediction system. Various aspects of predictability of the first and the second kind are highlighted for variables with long (for example, SST), and fast (for example, atmospheric) adjustment time scale. An additional focus of the analysis is how the predictability in the initialized coupled seasonal predictions compares with estimates based on the AMIP simulations. The results indicate that differences in the set up of AMIP simulations and coupled predictions, for example, representation of air?Csea interactions, and evolution of forecast spread from initial conditions do not change fundamental conclusion about the seasonal predictability. A discussion of the analysis presented herein, and its implications for the use of AMIP simulations for climate attribution, and for time-slice experiments to provide regional information, is also included.     

Sensitivity of multi-gas climate policy to emission metrics

The Global Warming Potential (GWP) index is currently used to create CO₂-equivalent emission totals for multi-gas greenhouse targets. While many alternatives have been proposed, it is not possible to uniquely define a metric that captures the different impacts of emissions of substances with widely disparate atmospheric lifetimes, which leads to a wide range of possible index values. We examine the sensitivity of emissions and climate outcomes to the value of the index used to aggregate methane emissions using a technologically detailed integrated assessment model. The methane index is varied between 4 and 70, with a central value of 21, which is the 100-year GWP value currently used in policy contexts. We find that the sensitivity to index value is, at most, 10–18 % in terms of methane emissions but only 2–3 % in terms of the maximum total radiative forcing change, with larger regional emissions differences in some cases. The choice of index also affects estimates of the cost of meeting a given end of century forcing target, with total two-gas mitigation cost increasing by 7–9 % if the index is increased, and increasing in most scenarios from 4 to 23 % if the index is lowered, with a slight (1 %) decrease in total cost in one case. We find that much of the methane abatement occurs as the induced effect of CO₂ abatement rather than explicit abatement, which is one reason why climate outcomes are relatively insensitive to the index value. We also find that the near-term climate benefit of increasing the methane index is small.     

On the relationship between Indian summer monsoon withdrawal and Indo-Pacific SST anomalies before and after 1976/1977 climate shift

A clear shift in the withdrawal dates of the Indian Summer Monsoon is observed in the long term time series of rainfall data. Prior (posterior) to the 1976/1977 climate shift most of the withdrawal dates are associated with a late (an early) withdrawal. As a result, the length of the rainy season (LRS) over the Indian land mass has also undergone similar changes (i.e., longer (shorter) LRS prior (posterior) to the climate shift). In this study, probable reasons for this significant shift in withdrawal dates and the LRS are investigated using reanalysis/observed datasets and also with the help of an atmospheric general circulation model. Reanalysis/observational datasets indicate that prior to the climate shift the sea surface temperature (SST) anomalies in the eastern equatorial Pacific Ocean and the Arabian Sea exerted a strong influence on both the withdrawal and the LRS. After the climate shift, the influence of the eastern equatorial Pacific Ocean SST has decreased and surprisingly, the influence of the Arabian Sea SST is almost non-existent. On the other hand, the influence of the southeastern equatorial Indian Ocean has increased significantly. It is observed that the upper tropospheric temperature gradient over the dominant monsoon region has decreased and the relative influence of the Indian Ocean SST variability on the withdrawal of the Indian Summer Monsoon has increased in the post climate shift period. Sensitivity experiments with the contrasting SST patterns on withdrawal dates and the LRS in the pre- and post- climate shift scenarios, confirm the observational evidences presented above.