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Abstract
A global search for areas where seasonal prediction may be feasible has attracted scientific interest for many years. This contribution is based primarily on data from a six-member ensemble of 45-yr climate runs, each of which is forced by observed sea surface temperatures (SSTs) and sea-ice extents and are unique only in their initial atmospheric conditions. The potentially predictable component of atmospheric interannual variability is assumed to be that due to oceanic forcing, and using “analysis of variance,” this is separated from the unpredictable internal component; potential predictability is measured as the ratio of ocean-forced variance to total variance. Significance levels and confidence intervals are calculated, both of which are essential for a meaningful interpretation of predictability estimates; the latter show there is low susceptibility to sampling problems here because of the large data source available.
Global maps of potential predictability, for simulated seasonal mean precipitation and mean sea level pressure (MSLP), are shown for both the solstitial and equinoctial seasons. In most regions, this model-based predictability estimate has large variations through the annual cycle. Not surprisingly, the highest predictability occurs over the tropical oceans, particularly the Atlantic and Pacific, for which a better knowledge of the influence of SSTs on diabatic heating is important for understanding the variability of teleconnected regions. Land areas displaying high predictability tend to support existing empirical studies, although over Australia and parts of Africa the model’s response to SSTs seems erroneously weak. In the midlatitude Northern Hemisphere, a winter–spring peak of predictability is confirmed, but a notable autumnal minima of predictability is also proposed. At polar latitudes, there is a small but significant influence of SSTs on spring MSLP, and in some localities a moderate influence on precipitation. Further work is required with observational data to properly assess these findings.
Abstract
A global search for areas where seasonal prediction may be feasible has attracted scientific interest for many years. This contribution is based primarily on data from a six-member ensemble of 45-yr climate runs, each of which is forced by observed sea surface temperatures (SSTs) and sea-ice extents and are unique only in their initial atmospheric conditions. The potentially predictable component of atmospheric interannual variability is assumed to be that due to oceanic forcing, and using “analysis of variance,” this is separated from the unpredictable internal component; potential predictability is measured as the ratio of ocean-forced variance to total variance. Significance levels and confidence intervals are calculated, both of which are essential for a meaningful interpretation of predictability estimates; the latter show there is low susceptibility to sampling problems here because of the large data source available.
Global maps of potential predictability, for simulated seasonal mean precipitation and mean sea level pressure (MSLP), are shown for both the solstitial and equinoctial seasons. In most regions, this model-based predictability estimate has large variations through the annual cycle. Not surprisingly, the highest predictability occurs over the tropical oceans, particularly the Atlantic and Pacific, for which a better knowledge of the influence of SSTs on diabatic heating is important for understanding the variability of teleconnected regions. Land areas displaying high predictability tend to support existing empirical studies, although over Australia and parts of Africa the model’s response to SSTs seems erroneously weak. In the midlatitude Northern Hemisphere, a winter–spring peak of predictability is confirmed, but a notable autumnal minima of predictability is also proposed. At polar latitudes, there is a small but significant influence of SSTs on spring MSLP, and in some localities a moderate influence on precipitation. Further work is required with observational data to properly assess these findings.
Abstract
A common signal in climate model projections is a decline in average summer rainfall over midlatitude continents due to anthropogenic warming. Most models suggest this rainfall decline will be less severe over North America than over Europe. This study aims to understand this difference in continental response and make inferences about its reliability. Data are primarily derived from a “perturbed physics” ensemble of models [Quantifying Uncertainty in Model Predictions project, subensemble S4 (QUMP-S4)] and are also compared with data from a multimodel ensemble [the Coupled Model Intercomparison Project phase 3 (CMIP3)]. A description of the uncertainty of predicted summer rainfall decline over both continents and its broad similarity between the two ensembles suggests the possibility that the QUMP-S4 ensemble may include many of the mechanisms that cause the differential continental response in the CMIP3 ensemble. Analysis of the QUMP-S4 mechanisms and their variability across the ensemble lead to the following conclusions. Over western North America, it is judged that the change in summer rainfall is more uncertain than models suggest, with a decline that could be either more or less severe than that over Europe. This is due to the western North American region’s dependence on uncertain modeling of high-elevation winter–spring surface hydrology. Over eastern North America, it seems likely that summer rainfall will decline. In particular, this decline is likely to be less severe than that over continental Europe since this difference primarily depends on reliable aspects of the models. However, a further, but speculative, conclusion is that these mechanisms could also lead to a larger increase in extreme rainfall events over eastern North America than over Europe.
Abstract
A common signal in climate model projections is a decline in average summer rainfall over midlatitude continents due to anthropogenic warming. Most models suggest this rainfall decline will be less severe over North America than over Europe. This study aims to understand this difference in continental response and make inferences about its reliability. Data are primarily derived from a “perturbed physics” ensemble of models [Quantifying Uncertainty in Model Predictions project, subensemble S4 (QUMP-S4)] and are also compared with data from a multimodel ensemble [the Coupled Model Intercomparison Project phase 3 (CMIP3)]. A description of the uncertainty of predicted summer rainfall decline over both continents and its broad similarity between the two ensembles suggests the possibility that the QUMP-S4 ensemble may include many of the mechanisms that cause the differential continental response in the CMIP3 ensemble. Analysis of the QUMP-S4 mechanisms and their variability across the ensemble lead to the following conclusions. Over western North America, it is judged that the change in summer rainfall is more uncertain than models suggest, with a decline that could be either more or less severe than that over Europe. This is due to the western North American region’s dependence on uncertain modeling of high-elevation winter–spring surface hydrology. Over eastern North America, it seems likely that summer rainfall will decline. In particular, this decline is likely to be less severe than that over continental Europe since this difference primarily depends on reliable aspects of the models. However, a further, but speculative, conclusion is that these mechanisms could also lead to a larger increase in extreme rainfall events over eastern North America than over Europe.
Abstract
A variety of regional and global sea surface temperature (SST) patterns are known to affect interannual to decadal variations of summer rainfall over the Sahel, and for most of these patterns considerable progress has been made towards understanding their influence. However, a possible link between Mediterranean SSTs and Sahelian rainfall has yet to be studied, and so the aim of this paper is to use observational and atmospheric general circulation model (AGCM) data to confirm and understand this relationship.
In years when the Mediterranean is warmer than average, it is shown that the Sahel tends to be wetter than normal, whereas in cool Mediterranean years it tends to be drier. The observed data also demonstrate that during the last five decades (1947–96) the strength of this impact has been roughly equal to that of Pacific SSTs, and a little less than that of the tropical Atlantic. Moreover it is most apparent on timescales of a decade or more, although it does also exert some influence at shorter timescales. It is also speculated that the Mediterranean may partly explain the impact of an interhemispheric pattern of SST anomalies found in earlier studies.
Analysis of the AGCM data provides the most convincing evidence that the observed relationship is indeed due to an influence of the Mediterranean on the Sahel. In particular, a pair of idealized experiments forced by warmer (colder) than average SSTs in the Mediterranean, and climatological SSTs elsewhere, produce a clear and significant summer rainfall response over the Sahel. Data from these experiments are then used to explain this impact. If SSTs in the Mediterranean are warmer than average, then local evaporation is enhanced, and the moisture content of the lower troposphere increases. This additional moisture is advected southward across the eastern Sahara by the mean flow, leading to enhanced low-level moisture convergence over the Sahel, which feeds enhanced rainfall. This is then amplified by four positive feedback mechanisms: a more rapid influx of moisture from the tropical Atlantic triggered by enhanced convective heating, a reduced outflow of moisture from the midlevel African easterly jet, an enhanced hydrological cycle, and a larger rainfall contribution from African easterly waves.
Abstract
A variety of regional and global sea surface temperature (SST) patterns are known to affect interannual to decadal variations of summer rainfall over the Sahel, and for most of these patterns considerable progress has been made towards understanding their influence. However, a possible link between Mediterranean SSTs and Sahelian rainfall has yet to be studied, and so the aim of this paper is to use observational and atmospheric general circulation model (AGCM) data to confirm and understand this relationship.
In years when the Mediterranean is warmer than average, it is shown that the Sahel tends to be wetter than normal, whereas in cool Mediterranean years it tends to be drier. The observed data also demonstrate that during the last five decades (1947–96) the strength of this impact has been roughly equal to that of Pacific SSTs, and a little less than that of the tropical Atlantic. Moreover it is most apparent on timescales of a decade or more, although it does also exert some influence at shorter timescales. It is also speculated that the Mediterranean may partly explain the impact of an interhemispheric pattern of SST anomalies found in earlier studies.
Analysis of the AGCM data provides the most convincing evidence that the observed relationship is indeed due to an influence of the Mediterranean on the Sahel. In particular, a pair of idealized experiments forced by warmer (colder) than average SSTs in the Mediterranean, and climatological SSTs elsewhere, produce a clear and significant summer rainfall response over the Sahel. Data from these experiments are then used to explain this impact. If SSTs in the Mediterranean are warmer than average, then local evaporation is enhanced, and the moisture content of the lower troposphere increases. This additional moisture is advected southward across the eastern Sahara by the mean flow, leading to enhanced low-level moisture convergence over the Sahel, which feeds enhanced rainfall. This is then amplified by four positive feedback mechanisms: a more rapid influx of moisture from the tropical Atlantic triggered by enhanced convective heating, a reduced outflow of moisture from the midlevel African easterly jet, an enhanced hydrological cycle, and a larger rainfall contribution from African easterly waves.
Abstract
This study provides an overview of the state of the art of modeling SST teleconnections to Africa and begins to investigate the sources of error. Data are obtained from the Coupled Model Intercomparison Project (CMIP) archives, phases 3 and 5 (CMIP3 and CMIP5), using the “20C3M” and “historical” coupled model experiments. A systematic approach is adopted, with the scope narrowed to six large-scale regions of sub-Saharan Africa within which seasonal rainfall anomalies are reasonably coherent, along with six SST modes known to affect these regions. No significant nonstationarity of the strength of these 6 × 6 teleconnections is found in observations. The capability of models to represent each teleconnection is then assessed (whereby half the teleconnections have observed SST–rainfall correlations that differ significantly from zero). A few of these teleconnections are found to be relatively easy to model, while a few more pose substantial challenges to models and many others exhibit a wide variety of model skill. Furthermore, some models perform consistently better than others, with the best able to at least adequately simulate 80%–85% of the 36 teleconnections. No improvement is found between CMIP3 and CMIP5. Analysis of atmosphere-only simulations suggests that the coupled model teleconnection errors may arise primarily from errors in their SST climatology and variability, although errors in the atmospheric component of teleconnections also play a role. Last, no straightforward relationship is found between the quality of a model's teleconnection to Africa and its SST or rainfall biases or its resolution. Perhaps not surprisingly, the causes of these errors are complex, and will require considerable further investigation.
Abstract
This study provides an overview of the state of the art of modeling SST teleconnections to Africa and begins to investigate the sources of error. Data are obtained from the Coupled Model Intercomparison Project (CMIP) archives, phases 3 and 5 (CMIP3 and CMIP5), using the “20C3M” and “historical” coupled model experiments. A systematic approach is adopted, with the scope narrowed to six large-scale regions of sub-Saharan Africa within which seasonal rainfall anomalies are reasonably coherent, along with six SST modes known to affect these regions. No significant nonstationarity of the strength of these 6 × 6 teleconnections is found in observations. The capability of models to represent each teleconnection is then assessed (whereby half the teleconnections have observed SST–rainfall correlations that differ significantly from zero). A few of these teleconnections are found to be relatively easy to model, while a few more pose substantial challenges to models and many others exhibit a wide variety of model skill. Furthermore, some models perform consistently better than others, with the best able to at least adequately simulate 80%–85% of the 36 teleconnections. No improvement is found between CMIP3 and CMIP5. Analysis of atmosphere-only simulations suggests that the coupled model teleconnection errors may arise primarily from errors in their SST climatology and variability, although errors in the atmospheric component of teleconnections also play a role. Last, no straightforward relationship is found between the quality of a model's teleconnection to Africa and its SST or rainfall biases or its resolution. Perhaps not surprisingly, the causes of these errors are complex, and will require considerable further investigation.
Abstract
Understanding the causes of regional climate projection uncertainty is a critical component toward establishing reliability of these projections. Here, four complementary experimental and decomposition techniques are synthesized to begin to understand which mechanisms differ most between models. These tools include a variety of multimodel ensembles, a decomposition of rainfall into tropics-wide or region-specific processes, and a separation of within-domain versus remote contributions to regional model projection uncertainty. Three East African regions are identified and characterized by spatially coherent intermodel projection behavior, which interestingly differs from previously identified regions of coherent interannual behavior. For the “Short Rains” regions, uncertainty in projected seasonal mean rainfall change is primarily due to uncertainties in the regional response to both the uniform and pattern components of SST warming (but not uncertainties in the global mean warming itself) and a small direct CO2 impact. These primarily derive from uncertain regional dynamics over both African and remote regions, rather than globally coherent (thermo)dynamics. For the “Long Rains” region, results are similar, except that uncertain atmospheric responses to a fixed SST pattern change are a little less important, and some key regional uncertainties are primarily located beyond Africa. The latter reflects the behavior of two outlying models that experience exceptional warming in the southern subtropical oceans, from which large lower-tropospheric moisture anomalies are advected by the mean flow to contribute to exceptional increases in the Long Rains totals. Further research could lead to a useful assessment of the reliability of these exceptional projections.
Abstract
Understanding the causes of regional climate projection uncertainty is a critical component toward establishing reliability of these projections. Here, four complementary experimental and decomposition techniques are synthesized to begin to understand which mechanisms differ most between models. These tools include a variety of multimodel ensembles, a decomposition of rainfall into tropics-wide or region-specific processes, and a separation of within-domain versus remote contributions to regional model projection uncertainty. Three East African regions are identified and characterized by spatially coherent intermodel projection behavior, which interestingly differs from previously identified regions of coherent interannual behavior. For the “Short Rains” regions, uncertainty in projected seasonal mean rainfall change is primarily due to uncertainties in the regional response to both the uniform and pattern components of SST warming (but not uncertainties in the global mean warming itself) and a small direct CO2 impact. These primarily derive from uncertain regional dynamics over both African and remote regions, rather than globally coherent (thermo)dynamics. For the “Long Rains” region, results are similar, except that uncertain atmospheric responses to a fixed SST pattern change are a little less important, and some key regional uncertainties are primarily located beyond Africa. The latter reflects the behavior of two outlying models that experience exceptional warming in the southern subtropical oceans, from which large lower-tropospheric moisture anomalies are advected by the mean flow to contribute to exceptional increases in the Long Rains totals. Further research could lead to a useful assessment of the reliability of these exceptional projections.
Abstract
Convection-permitting (CP) models promise much in response to the demand for increased localization of future climate information: greater resolution of influential land surface characteristics, improved representation of convective storms, and unprecedented resolution of user-relevant data. In practice, however, it is contended that the benefits of enhanced resolution cannot be fully realized due to the gap between models’ computational and effective resolution. Nevertheless, where surface forcing is strongly heterogeneous, one can argue that usable information may persist close to the grid scale. Here we analyze a 4.5-km resolution CP projection for Africa, asking whether and where fine-scale projection detail is robust at sub-25-km scales, focusing on geolocated rainfall features (rather than Lagrangian motion). Statistically significant detail for seasonal means and daily extremes is most frequent in regions of high topographic variability, most prominently in East Africa throughout the annual cycle, West Africa in the monsoon season, and to a lesser extent over Southern Africa. Lake coastal features have smaller but significant impacts on projection detail, whereas ocean coastlines and urban conurbations have little or no detectable impact. The amplitude of this sub-25-km projection detail can be similar to that of the local climatology in mountainous regions (or around a third near East Africa’s lake shores), so potentially beneficial for improved localization of future climate information. In flatter regions distant from coasts (the majority of Africa), spatial heterogeneity can be explained by chaotic weather variability. Here, the robustness of local climate projection information can be substantially enhanced by spatial aggregation to approximately 25-km scales, especially for daily extremes and equatorial regions.
Significance Statement
Recent substantial increases in the horizontal resolution of climate models bring the potential for both more reliable and more local future climate information. However, the best spatial scale on which to analyze such data for impacts assessments remains unclear. We examine a 4.5-km resolution climate projection for Africa, focusing on seasonal and daily rainfall. Spatially fixed fine-scale projection detail is found to be statistically robust at sub-25-km scales in the most mountainous regions and to a lesser extent along lake coastlines. Elsewhere, the model data may be better aggregated to at least 25-km scales to reduce sampling uncertainties. Such evolving guidance on the circumstances and extent of high-resolution data aggregation will help users gain greater benefit from climate model projections.
Abstract
Convection-permitting (CP) models promise much in response to the demand for increased localization of future climate information: greater resolution of influential land surface characteristics, improved representation of convective storms, and unprecedented resolution of user-relevant data. In practice, however, it is contended that the benefits of enhanced resolution cannot be fully realized due to the gap between models’ computational and effective resolution. Nevertheless, where surface forcing is strongly heterogeneous, one can argue that usable information may persist close to the grid scale. Here we analyze a 4.5-km resolution CP projection for Africa, asking whether and where fine-scale projection detail is robust at sub-25-km scales, focusing on geolocated rainfall features (rather than Lagrangian motion). Statistically significant detail for seasonal means and daily extremes is most frequent in regions of high topographic variability, most prominently in East Africa throughout the annual cycle, West Africa in the monsoon season, and to a lesser extent over Southern Africa. Lake coastal features have smaller but significant impacts on projection detail, whereas ocean coastlines and urban conurbations have little or no detectable impact. The amplitude of this sub-25-km projection detail can be similar to that of the local climatology in mountainous regions (or around a third near East Africa’s lake shores), so potentially beneficial for improved localization of future climate information. In flatter regions distant from coasts (the majority of Africa), spatial heterogeneity can be explained by chaotic weather variability. Here, the robustness of local climate projection information can be substantially enhanced by spatial aggregation to approximately 25-km scales, especially for daily extremes and equatorial regions.
Significance Statement
Recent substantial increases in the horizontal resolution of climate models bring the potential for both more reliable and more local future climate information. However, the best spatial scale on which to analyze such data for impacts assessments remains unclear. We examine a 4.5-km resolution climate projection for Africa, focusing on seasonal and daily rainfall. Spatially fixed fine-scale projection detail is found to be statistically robust at sub-25-km scales in the most mountainous regions and to a lesser extent along lake coastlines. Elsewhere, the model data may be better aggregated to at least 25-km scales to reduce sampling uncertainties. Such evolving guidance on the circumstances and extent of high-resolution data aggregation will help users gain greater benefit from climate model projections.
Abstract
Squall lines (SLs) form an important component of the meteorology of northern Africa, and in particular, contribute substantially to rainfall totals. Their generation requires the existence of a potentially unstable low-level supply of moisture overlain by dry desert air and vertical wind shear beneath the midlevel African easterly jet. The instability may be released (and an SL initiated) by factors such as surface heating, topography, African waves, or surface evaporation. The relative importance of each of these factors and the means by which they impact on SL generation is reviewed. This is followed by a detailed analysis of one month of satellite imagery and surface data for August 1985 over a portion of central northern Africa.
The novelty of our study lies in the temporal resolution of the satellite imagery, which with 21 images per day allows the identification of a large number of short-lived SLs (4-h duration or less). On the southern fringes of the Sahara these are likely to contribute significantly to rainfall totals, and so cannot be neglected. The analysis is also entirely objective, an important feature if future studies are to produce a homogeneous SL climatology. Our results show, for the period and area of study, a preference for SLs to generate during the midafternoon, with generation probability also enhanced by above-average low-level westerly flow and by surface features such as the Air Mountains, the Jos Plateau, and the northernmost section of the river Niger. African waves and the strength of the African easterly jet were not found to affect SL generation for the period and region studied. Where these results do not support previous studies, we speculate that this may be due to differences in location or time of year, but only a more extensive analysis will resolve these issues.
Abstract
Squall lines (SLs) form an important component of the meteorology of northern Africa, and in particular, contribute substantially to rainfall totals. Their generation requires the existence of a potentially unstable low-level supply of moisture overlain by dry desert air and vertical wind shear beneath the midlevel African easterly jet. The instability may be released (and an SL initiated) by factors such as surface heating, topography, African waves, or surface evaporation. The relative importance of each of these factors and the means by which they impact on SL generation is reviewed. This is followed by a detailed analysis of one month of satellite imagery and surface data for August 1985 over a portion of central northern Africa.
The novelty of our study lies in the temporal resolution of the satellite imagery, which with 21 images per day allows the identification of a large number of short-lived SLs (4-h duration or less). On the southern fringes of the Sahara these are likely to contribute significantly to rainfall totals, and so cannot be neglected. The analysis is also entirely objective, an important feature if future studies are to produce a homogeneous SL climatology. Our results show, for the period and area of study, a preference for SLs to generate during the midafternoon, with generation probability also enhanced by above-average low-level westerly flow and by surface features such as the Air Mountains, the Jos Plateau, and the northernmost section of the river Niger. African waves and the strength of the African easterly jet were not found to affect SL generation for the period and region studied. Where these results do not support previous studies, we speculate that this may be due to differences in location or time of year, but only a more extensive analysis will resolve these issues.
Abstract
The importance of investigating regional climate changes associated with degrees of global warming is increasingly being recognized, but the majority of relevant research has been based on multimodel ensembles (MMEs) from the Coupled Model Intercomparison Project (CMIP). This has left two important questions unanswered: Are there plausible futures which are not represented by the models in CMIP? And, how would regional climates evolve under enhanced global warming, beyond 4°C? In this paper, two perturbed physics ensembles (PPEs) are used to address these issues with reference to African precipitation. Examination of model versions that generate warming greater than 4°C in the twenty-first century shows that changes in African precipitation are enhanced gradually, even to high global temperatures; however, there may be nonlinearities that are not incorporated here due to limited model complexity. The range of projections from the PPEs is compared to data from phases 3 and 5 of CMIP (CMIP3 and CMIP5), revealing regional differences. This is partly the result of implausible model versions, but the PPE dataset can be justifiably constrained given its size and systematic nature, highlighting an additional advantage over MMEs. After applying constraints, the PPEs still show changes that are outside the range of CMIP, most prominently strong dry signals in west equatorial Africa and the Sahel, implying that MMEs may underestimate risks for these regions. Analysis of African precipitation changes therefore demonstrates that regional assessments that rely on CMIP3 and CMIP5 may overlook uncertainties associated with model parameterizations and pronounced warming. More systematic approaches are needed for conservative estimates of danger.
Abstract
The importance of investigating regional climate changes associated with degrees of global warming is increasingly being recognized, but the majority of relevant research has been based on multimodel ensembles (MMEs) from the Coupled Model Intercomparison Project (CMIP). This has left two important questions unanswered: Are there plausible futures which are not represented by the models in CMIP? And, how would regional climates evolve under enhanced global warming, beyond 4°C? In this paper, two perturbed physics ensembles (PPEs) are used to address these issues with reference to African precipitation. Examination of model versions that generate warming greater than 4°C in the twenty-first century shows that changes in African precipitation are enhanced gradually, even to high global temperatures; however, there may be nonlinearities that are not incorporated here due to limited model complexity. The range of projections from the PPEs is compared to data from phases 3 and 5 of CMIP (CMIP3 and CMIP5), revealing regional differences. This is partly the result of implausible model versions, but the PPE dataset can be justifiably constrained given its size and systematic nature, highlighting an additional advantage over MMEs. After applying constraints, the PPEs still show changes that are outside the range of CMIP, most prominently strong dry signals in west equatorial Africa and the Sahel, implying that MMEs may underestimate risks for these regions. Analysis of African precipitation changes therefore demonstrates that regional assessments that rely on CMIP3 and CMIP5 may overlook uncertainties associated with model parameterizations and pronounced warming. More systematic approaches are needed for conservative estimates of danger.
Abstract
Projected changes in regional seasonal precipitation due to climate change are highly uncertain, with model disagreement on even the sign of change in many regions. Using a 20-member CMIP5 ensemble under the RCP8.5 scenario, the intermodel uncertainty of the spatial patterns of projected end-of-twenty-first-century change in precipitation is found not to be strongly influenced by uncertainty in global mean temperature change. In the tropics, both the ensemble mean and intermodel uncertainty of regional precipitation change are found to be predominantly related to spatial shifts in convection and convergence, associated with processes such as sea surface temperature (SST) pattern change and land–sea thermal contrast change. The authors hypothesize that the zonal-mean seasonal migration of these shifts is driven by 1) the nonlinear spatial response of convection to SST changes and 2) a general movement of convection from land to ocean in response to SST increases. Assessment of tropical precipitation model projections over East Africa highlights the complexity of regional rainfall changes. Thermodynamically driven moisture increases determine the magnitude of the long rains (March–May) ensemble mean precipitation change in this region, whereas model uncertainty in spatial shifts of convection accounts for almost all of the intermodel uncertainty. Moderate correlations are found across models between the long rains precipitation change and patterns of SST change in the Pacific and Indian Oceans. Further analysis of the capability of models to represent present-day SST–rainfall links, and any relationship with model projections, may contribute to constraining the uncertainty in projected East Africa long rains precipitation.
Abstract
Projected changes in regional seasonal precipitation due to climate change are highly uncertain, with model disagreement on even the sign of change in many regions. Using a 20-member CMIP5 ensemble under the RCP8.5 scenario, the intermodel uncertainty of the spatial patterns of projected end-of-twenty-first-century change in precipitation is found not to be strongly influenced by uncertainty in global mean temperature change. In the tropics, both the ensemble mean and intermodel uncertainty of regional precipitation change are found to be predominantly related to spatial shifts in convection and convergence, associated with processes such as sea surface temperature (SST) pattern change and land–sea thermal contrast change. The authors hypothesize that the zonal-mean seasonal migration of these shifts is driven by 1) the nonlinear spatial response of convection to SST changes and 2) a general movement of convection from land to ocean in response to SST increases. Assessment of tropical precipitation model projections over East Africa highlights the complexity of regional rainfall changes. Thermodynamically driven moisture increases determine the magnitude of the long rains (March–May) ensemble mean precipitation change in this region, whereas model uncertainty in spatial shifts of convection accounts for almost all of the intermodel uncertainty. Moderate correlations are found across models between the long rains precipitation change and patterns of SST change in the Pacific and Indian Oceans. Further analysis of the capability of models to represent present-day SST–rainfall links, and any relationship with model projections, may contribute to constraining the uncertainty in projected East Africa long rains precipitation.
Abstract
A study of the impact of ENSO in the Hadley Centre’s atmospheric climate model HADAM1 is presented, with emphasis on the North Pacific–American (NPA) sector. The study is based both on observational data and an ensemble of six integrations for the period 1949–93, forced with observed global sea-ice and sea surface temperature data. The model is shown to reproduce most of the known features of the worldwide atmospheric response to ENSO in boreal winter (January–March).
Focusing on the NPA sector, the leading modes of low-frequency weather variability in the winter season are identified on their natural timescales for both the modeled and observed atmospheres. These modes are analyzed via rotated EOF analysis of daily 500-hPa height data, filtered to remove synoptic timescale variations. The model gives a reasonably skillful simulation of the main features of the four leading modes in the NPA region:the Pacific–North American (PNA), the west Pacific (WP), the east Pacific (EP), and the North Pacific (NP) modes. The sensitivity of these modes to SSTs is investigated. In particular, sensitivity to SSTs associated with ENSO is analyzed in terms of the shift in frequency of occurrence of the opposing phases of a mode between warm event (El Niño) and cold event (La Niña) years. Three of the observed modes show such a sensitivity: the PNA, WP, and NP modes. Of the corresponding model modes, only the PNA responds significantly to ENSO (but too strongly in warm event years), which is clearly illustrated by changes in both the frequency and duration of PNA episodes between warm and cold event years. The EP mode shows no sensitivity to ENSO, in either model or observed atmospheres. Finally, although the model is able to reproduce the pattern of decadal anomalies seen in the North Pacific in the years 1977–87, which is related to the prevalence of the positive phase of the PNA in this period, it does so with a much reduced amplitude; possible reasons for this discrepancy are discussed.
Abstract
A study of the impact of ENSO in the Hadley Centre’s atmospheric climate model HADAM1 is presented, with emphasis on the North Pacific–American (NPA) sector. The study is based both on observational data and an ensemble of six integrations for the period 1949–93, forced with observed global sea-ice and sea surface temperature data. The model is shown to reproduce most of the known features of the worldwide atmospheric response to ENSO in boreal winter (January–March).
Focusing on the NPA sector, the leading modes of low-frequency weather variability in the winter season are identified on their natural timescales for both the modeled and observed atmospheres. These modes are analyzed via rotated EOF analysis of daily 500-hPa height data, filtered to remove synoptic timescale variations. The model gives a reasonably skillful simulation of the main features of the four leading modes in the NPA region:the Pacific–North American (PNA), the west Pacific (WP), the east Pacific (EP), and the North Pacific (NP) modes. The sensitivity of these modes to SSTs is investigated. In particular, sensitivity to SSTs associated with ENSO is analyzed in terms of the shift in frequency of occurrence of the opposing phases of a mode between warm event (El Niño) and cold event (La Niña) years. Three of the observed modes show such a sensitivity: the PNA, WP, and NP modes. Of the corresponding model modes, only the PNA responds significantly to ENSO (but too strongly in warm event years), which is clearly illustrated by changes in both the frequency and duration of PNA episodes between warm and cold event years. The EP mode shows no sensitivity to ENSO, in either model or observed atmospheres. Finally, although the model is able to reproduce the pattern of decadal anomalies seen in the North Pacific in the years 1977–87, which is related to the prevalence of the positive phase of the PNA in this period, it does so with a much reduced amplitude; possible reasons for this discrepancy are discussed.
