Walker circulation controls ENSO atmospheric feedbacks in uncoupled and coupled climate model simulations

Many climate models strongly underestimate the two most important atmospheric feedbacks operating in El Niño/Southern Oscillation (ENSO), the positive (amplifying) zonal surface wind feedback and negative (damping) surface-heat flux feedback (hereafter ENSO atmospheric feedbacks, EAF). This hampers a realistic representation of ENSO dynamics in these models. Here we show that the atmospheric components of climate models participating in the 5th phase of the Coupled Model Intercomparison Project (CMIP5) when forced by observed sea surface temperatures (SST), already underestimate EAF on average by 23%, but less than their coupled counterparts (on average by 54%). There is a pronounced tendency of atmosphere models to simulate stronger EAF, when they exhibit a stronger mean deep convection and enhanced cloud cover over the western equatorial Pacific (WEP), indicative of a stronger rising branch of the Pacific Walker Circulation (PWC). Further, differences in the mean deep convection over the WEP between the coupled and uncoupled models explain a large part of the differences in EAF, with the deep convection in the coupled models strongly depending on the equatorial Pacific SST bias. Experiments with a single atmosphere model support the relation between the equatorial Pacific atmospheric mean state, the SST bias and the EAF. An implemented cold SST bias in the observed SST forcing weakens deep convection and reduces cloud cover in the rising branch of the PWC, causing weaker EAF. A warm SST bias has the opposite effect. Our results elucidate how biases in the mean state of the PWC and equatorial SST hamper a realistic simulation of the EAF.     

An evaluation of the simulated water balance of Eurasia and northern Africa at 6000 y BP using lake status data

Changes in the water balance of Eurasia and northern Africa in response to insolation forcing at 6000 y BP simulated by five atmospheric general circulation models have been compared with observations of changes in lake status. All of the simulations show enhancement of the Asian summer monsoon and of the high pressure cells over the Pacific and Central Asia and the Middle East, causing wetter conditions in northern India and southern China and drier conditions along the Chinese coast and west of the monsoon core. All of the models show enhancement of the African monsoon, causing wetter conditions in the zone between ca 10–20 °N. Four of the models show conditions wetter than present in southern Europe and drier than present in northern Europe. Three of the models show conditions similar to present in the mid-latitude continental interior, while the remaining models show conditions somewhat drier than present. The extent and location of each of the simulated changes varies between the models, as does the mechanism producing these changes. The lake data confirm some features of the simulations, but indicate discrepancies between observed and simulated climates. For example, the data show: (1) conditions wetter than present in central Asia, from India to northern China and Mongolia, indicating that the simulated Asian monsoon expansion is too small; (2) conditions wetter than present between ca. 10–30 °N in Africa, indicating that the simulated African monsoon expansion is too small; (3) that northern Europe was drier, but the area of significantly drier conditions was more localized (around the Baltic) than shown in the simulations; (4) that southern Europe was wetter than present, apparently consistent with the simulations, but pollen data suggest that this reflects an increase in summer rainfall whereas the models show winter precipitation, and (5) that the mid-latitude continental interior was generally wetter than present. Received: 29 March 1996 / Accepted: 31 May 1996     

Review of simulations of climate variability and change with the GFDL R30 coupled climate model

A review is presented of the development and simulation characteristics of the most recent version of a global coupled model for climate variability and change studies at the Geophysical Fluid Dynamics Laboratory, as well as a review of the climate change experiments performed with the model. The atmospheric portion of the coupled model uses a spectral technique with rhomboidal 30 truncation, which corresponds to a transform grid with a resolution of approximately 3.75° longitude by 2.25° latitude. The ocean component has a resolution of approximately 1.875° longitude by 2.25° latitude. Relatively simple formulations of river routing, sea ice, and land surface processes are included. Two primary versions of the coupled model are described, differing in their initialization techniques and in the specification of sub-grid scale oceanic mixing of heat and salt. For each model a stable control integration of near millennial scale duration has been conducted, and the characteristics of both the time-mean and variability are described and compared to observations. A review is presented of a suite of climate change experiments conducted with these models using both idealized and realistic estimates of time-varying radiative forcing. Some experiments include estimates of forcing from past changes in volcanic aerosols and solar irradiance. The experiments performed are described, and some of the central findings are highlighted. In particular, the observed increase in global mean surface temperature is largely contained within the spread of simulated global mean temperatures from an ensemble of experiments using observationally-derived estimates of the changes in radiative forcing from increasing greenhouse gases and sulfate aerosols.     

Effects of Amazonian deforestation on climate: a numerical experiment with a coupled biosphere-atmosphere model with soil hydrology

Summary A coupled biosphere-atmosphere statistical-dynamical model (SDM) is used to study the climatic effects of Amazonian deforestation. A soil moisture model based on BATS has been incorporated into the SDM in order to study the biogeophysical feedback of change in surface characteristics to regional climate due to the deforestation. In the control experiment, the mean annual and mean seasonal climate is well simulated by the model when compared with NCEP/NCAR reanalysis data. In the deforestation experiment, the evergreen broadleaf trees in the Amazonian region are substituted by short grass. The effects of Amazonian deforestation on regional climate are analysed taking into account the model simulations for the land portion of the latitude belts comprising the tropical region. Amazonian deforestation results in regional climate changes such as a decrease in evaporation, precipitation, available surface net radiation and soil moisture content, and an increase in temperatures and sensible heat flux. The reduction in transpiration was responsible for the most part of the decrease in total evapotranspiration. The reduction in precipitation was larger than the decrease in evapotranspiration so that runoff was reduced. The simulation of the diurnal cycle of the surface temperature shows an increase in temperature during the day and a decrease at night, which is in agreement with observations, whereas earlier GCM experiments showed an increase both during the day and night. In general, the changes in temperature and energy fluxes are in good agreement with GCM experiments, showing that the SDM is able to simulate the characteristics of the tropical climate that are associated with the substitution of forest by pasture areas.     

Fully coupled climate/dynamical vegetation model simulations over Northern Africa during the mid-Holocene

 The climate and vegetation patterns of the middle Holocene (6000 years ago; 6 ka) over Northern Africa are simulated using a fully-synchronous climate and dynamical vegetation model. The coupled model predicts a northward shift in tropical rainforest and tropical deciduous forest vegetation by about 5 degrees of latitude, and an increase in grassland at the present-day simulated Saharan boundaries. The northward expansion of vegetation over North Africa at 6 ka is initiated by an orbitally-induced amplification of the summer monsoon, and enhanced by feedback effects induced by the vegetation. These combined processes lead to a major reduction in Saharan desert area at 6 ka relative to present-day of about 50%. However, as shown in previous asynchronous modelling studies, the coupled climate/vegetation model does not fully reproduce the vegetation patterns inferred from palaeoenvironmental records, which suggest that steppe vegetation may have existed across most of Northern Africa. Orbital changes produce an intensification of monsoonal precipitation during the peak rainy season (July to September), whilst vegetation feedbacks, in addition to producing further increases in the peak intensity, play an important role in extending the rainy season from May/June through to November. The orbitally induced increases in precipitation are relatively uniform from west to east, in contrast to vegetation feedback-induced increases in precipitation which are concentrated in western North Africa. Annual-average precipitation increases caused by vegetation feedbacks are simulated to be of similar importance to orbital effects in the west, whilst they are relatively unimportant farther to the east. The orbital, vegetation and combined orbital and vegetation-induced changes in climate, from the simulations presented in this study, have been compared with results from previous modelling studies over the appropriate North African domain. Consequently, the important role of vegetation parametrizations in determining the magnitude of vegetation feedbacks has been illustrated. Further modelling studies which include the effects of changes in ocean temperature and changes in soil properties may be needed, along with additional observations, to resolve the discrepancy between model predictions of vegetation and palaeorecords for North Africa. Received: 15 June 1999 / Accepted: 14 December 1999     

Quantifying internal variability in a regional climate model: a case study for Southern Africa

This study aims at presenting various methodologies to separate the reproducible and irreproducible components of seasonal and intraseasonal climate variability simulated by a regional climate model over Southern Africa (south of 15°S), during an austral summer rainy season representative of the climatology. To that end, a 30-member ensemble simulation is performed using WRF laterally forced by the ERA40 reanalyses. Retained metrics include the analysis of weather regimes, signal-to-noise ratio, inter-member standard deviation and coefficient of variation. At the seasonal timescale, simulated rainfall amounts generally show a strong reproducibility, except in the subtropics and over the southern part of the Mozambique Channel. There, the number of rainy days is roughly similar in all members, while their average intensity varies extensively. At the intraseasonal timescale, the chronology of weather regimes, derived from the 500?hPa geopotential height, is highly reproducible. Rainfall variability is much less reproducible, especially in the central parts of the domain and near its outflow boundaries. Analysis of a South African regional index nonetheless indicates that both wet and dry spells tend to be accurately simulated and occur in phase in most members, demonstrating that they are embedded in large-scale variability patterns. Internal variability is lastly related to the lateral forcings along the domain boundaries. An objective classification of inflow/outflow mass fluxes allows identification of the recurrent synoptic configurations that favor strong or weak regional reproducibility. The main uncertainties concern the basic morphological features of rain-bearing systems (i.e., their spatial extension, location and propagation speed). Consequences for tropical-temperate interactions are then discussed.     

Sea ice in the Barents Sea: seasonal to interannual variability and climate feedbacks in a global coupled model

Sea ice variability in the Barents Sea and its impact on climate are analyzed using a 465-year control integration of a global coupled atmosphere–ocean–sea ice model. Sensitivity simulations are performed to investigate the response to an isolated sea ice anomaly in the Barents Sea. The interannual variability of sea ice volume in the Barents Sea is mainly determined by variations in sea ice import into Barents Sea from the Central Arctic. This import is primarily driven by the local wind field. Horizontal oceanic heat transport into the Barents Sea is of minor importance for interannual sea ice variations but is important on longer time scales. Events with strong positive sea ice anomalies in the Barents Sea are due to accumulation of sea ice by enhanced sea ice imports and related NAO-like pressure conditions in the years before the event. Sea ice volume and concentration stay above normal in the Barents Sea for about 2 years after an event. This strongly increases the albedo and reduces the ocean heat release to the atmosphere. Consequently, air temperature is much colder than usual in the Barents Sea and surrounding areas. Precipitation is decreased and sea level pressure in the Barents Sea is anomalously high. The large-scale atmospheric response is limited with the main impact being a reduced pressure over Scandinavia in the year after a large ice volume occurs in the Barents Sea. Furthermore, high sea ice volume in the Barents Sea leads to increased sea ice melting and hence reduced surface salinity. Generally, the climate response is smallest in summer and largest in winter and spring.     

Quantifying contributions of climate feedbacks to tropospheric warming in the NCAR CCSM3.0

In this study, a coupled atmosphere-surface “climate feedback-response analysis method” (CFRAM) was applied to the slab ocean model version of the NCAR CCSM3.0 to understand the tropospheric warming due to a doubling of CO₂ concentration through quantifying the contributions of each climate feedback process. It is shown that the tropospheric warming displays distinct meridional and vertical patterns that are in a good agreement with the multi-model mean projection from the IPCC AR4. In the tropics, the warming in the upper troposphere is stronger than in the lower troposphere, leading to a decrease in temperature lapse rate, whereas in high latitudes the opposite it true. In terms of meridional contrast, the lower tropospheric warming in the tropics is weaker than that in high latitudes, resulting in a weakened meridional temperature gradient. In the upper troposphere the meridional temperature gradient is enhanced due to much stronger warming in the tropics than in high latitudes. Using the CFRAM method, we analyzed both radiative feedbacks, which have been emphasized in previous climate feedback analysis, and non-radiative feedbacks. It is shown that non-radiative (radiative) feedbacks are the major contributors to the temperature lapse rate decrease (increase) in the tropical (polar) region. Atmospheric convection is the leading contributor to temperature lapse rate decrease in the tropics. The cloud feedback also has non-negligible contributions. In the polar region, water vapor feedback is the main contributor to the temperature lapse rate increase, followed by albedo feedback and CO₂ forcing. The decrease of meridional temperature gradient in the lower troposphere is mainly due to strong cooling from convection and cloud feedback in the tropics and the strong warming from albedo feedback in the polar region. The strengthening of meridional temperature gradient in the upper troposphere can be attributed to the warming associated with convection and cloud feedback in the tropics. Since convection is the leading contributor to the warming differences between tropical lower and upper troposphere, and between the tropical and polar regions, this study indicates that tropical convection plays a critical role in determining the climate sensitivity. In addition, the CFRAM analysis shows that convective process and water vapor feedback are the two major contributors to the tropical upper troposphere temperature change, indicating that the excessive upper tropospheric warming in the IPCC AR4 models may be due to overestimated warming from convective process or underestimated cooling due to water vapor feedback.     

Assessing the role of deep rooted vegetation in the climate system with model simulations: mechanism, comparison to observations and implications for Amazonian deforestation

Deep rooted vegetation (of up to 68?m) has been found in many parts of the tropics. However, models of the general atmospheric circulation (GCMs) typically use rooting depths of less than 2?m in their land surface parametrizations. How does the incorporation of deep roots into such a model affect the simulated climate? We assess this question by using a GCM and find that deeper roots lead to a pronounced seasonal response. During the dry season, evapotranspiration and the associated latent heat flux are considerably increased over large regions leading to a cooling of up to 8?K. The enhanced atmospheric moisture is transported towards the main convection areas in the inner tropical convergence zone where it supplies more energy to convection thus intensifying the tropical circulation patterns. Comparison to different kinds of data reveals that the simulation with deeper roots is much closer to observations. The inclusion of deep roots also leads to a general increased climatic sensitivity to rooting depth change. We investigate this aspect in the context of the climatic effects of large-scale deforestation in Amazonia. Most of the regional and remote changes can be attributed to the removal of deep roots. We conclude that deep rooted vegetation is an important part of the tropical climate system. Without the consideration of deep roots, the present-day surface climate cannot adequately be simulated.     

Variability of Fram Strait sea ice export: causes, impacts and feedbacks in a coupled climate model

Analyses of a 500-year control integration of the global coupled atmosphere–sea ice–ocean model ECHAM5.0/MPI-OM show a high variability in the ice export through Fram Strait on interannual to decadal timescales. This variability is mainly determined by variations in the sea level pressure gradient across Fram Strait and thus geostrophic wind stress. Ice thickness anomalies, formed at the Siberian coast and in the Chukchi Sea, propagate across the Arctic to Fram Strait and contribute to the variability of the ice export on a timescale of about 9 years. Large anomalies of the ice export through Fram Strait cause fresh water signals, which reach the Labrador Sea after 1–2 years and lead to significant changes in the deep convection. The associated anomalies in ice cover and ocean heat release have a significant impact on air temperature in the Labrador Sea and on the large-scale atmospheric circulation. This affects the sea ice transport and distribution in the Arctic again. Sensitivity studies, simulating the effect of large ice exports through Fram Strait, show that the isolated effect of a prescribed ice/fresh water anomaly is very important for the climate variability in the Labrador Sea. Thus, the ice export through Fram Strait can be used for predictability of Labrador Sea climate up to 2 years in advance.     

Climate model simulation of anthropogenic influence on greenhouse-induced climate change (early agriculture to modern): the role of ocean feedbacks

We present further steps in our analysis of the early anthropogenic hypothesis (Ruddiman, Clim Change 61:261–293, 2003) that increased levels of greenhouse gases in the current interglacial, compared to lower levels in previous interglacials, were initiated by early agricultural activities, and that these increases caused a warming of climate long before the industrial era (～1750). These steps include updating observations of greenhouse gas and climate trends from earlier interglacials, reviewing recent estimates of greenhouse gas emissions from early agriculture, and describing a simulation by a climate model with a dynamic ocean forced by the low levels of greenhouse gases typical of previous interglacials in order to gauge the magnitude of the climate change for an inferred (natural) low greenhouse gas level relative to a high present day level. We conduct two time slice (equilibrium) simulations using present day orbital forcing and two levels of greenhouse gas forcing: the estimated low (natural) levels of previous interglacials, and the high levels of the present (control). By comparing the former to the latter, we estimate how much colder the climate would be without the combined greenhouse gas forcing of the early agriculture era (inferred from differences between this interglacial and previous interglacials) and the industrial era (the period since ～1750). With the low greenhouse gas levels, the global average surface temperature is 2.7 K lower than present day—ranging from ～2 K lower in the tropics to 4–8 K lower in polar regions. These changes are large, and larger than those reported in a pre-industrial (～1750) simulation with this model, because the imposed low greenhouse gas levels (CH₄ = 450 ppb, CO₂ = 240 ppm) are lower than both pre-industrial (CH₄ = 760 ppb, CO₂ = 280 ppm) and modern control (CH₄ = 1,714 ppb, CO₂ = 355 ppm) values. The area of year-round snowcover is larger, as found in our previous simulations and some other modeling studies, indicating that a state of incipient glaciation would exist given the current configuration of earth’s orbit (reduced insolation in northern hemisphere summer) and the imposed low levels of greenhouse gases. We include comparisons of these snowcover maps with known locations of earlier glacial inception and with locations of twentieth century glaciers and ice caps. In two earlier studies, we used climate models consisting of atmosphere, land surface, and a shallow mixed-layer ocean (Ruddiman et al., Quat Sci Rev 25:1–10, 2005; Vavrus et al., Quat Sci Rev 27:1410–1425, 2008). Here, we replaced the mixed-layer ocean with a complete dynamic ocean. While the simulated climate of the atmosphere and the surface with this improved model configuration is similar to our earlier results (Vavrus et al., Quat Sci Rev 27:1410–1425, 2008), the added information from the full dynamical ocean is of particular interest. The global and vertically-averaged ocean temperature is 1.25 K lower, the area of sea ice is larger, and there is less upwelling in the Southern Ocean. From these results, we infer that natural ocean feedbacks could have amplified the greenhouse gas changes initiated by early agriculture and possibly account for an additional increment of CO₂ increase beyond that attributed directly to early agricultural, as proposed by Ruddiman (Rev Geophys 45:RG4001, 2007). However, a full test of the early anthropogenic hypothesis will require additional observations and simulations with models that include ocean and land carbon cycles and other refinements elaborated herein.     

Uncertainties in regional climate change prediction: a regional analysis of ensemble simulations with the HADCM2 coupled AOGCM

We analyze ensembles (four realizations) of historical and future climate transient experiments carried out with the coupled atmosphere-ocean general circulation model (AOGCM) of the Hadley Centre for Climate Prediction and Research, version HADCM2, with four scenarios of greenhouse gas (GHG) and sulfate forcing. The analysis focuses on the regional scale, and in particular on 21 regions covering all land areas in the World (except Antarctica). We examine seasonally averaged surface air temperature and precipitation for the historical period of 1961–1990 and the future climate period of 2046–2075. Compared to previous AOGCM simulations, the HADCM2 model shows a good performance in reproducing observed regional averages of summer and winter temperature and precipitation. The model, however, does not reproduce well observed interannual variability. We find that the uncertainty in regional climate change predictions associated with the spread of different realizations in an ensemble (i.e. the uncertainty related to the internal model variability) is relatively low for all scenarios and regions. In particular, this uncertainty is lower than the uncertainty due to inter-scenario variability and (by comparison with previous regional analyses of AOGCMs) with inter-model variability. The climate biases and sensitivities found for different realizations of the same ensemble were similar to the corresponding ensemble averages and the averages associated with individual realizations of the same ensemble did not differ from each other at the 5% confidence level in the vast majority of cases. These results indicate that a relatively small number of realizations (3 or 4) is sufficient to characterize an AOGCM transient climate change prediction at the regional scale. Received: 12 January 1998 / Accepted: 7 July 1999     

Impact of bio-physical feedbacks on the tropical climate in coupled and uncoupled GCMs

The bio-physical feedback process between the marine ecosystem and the tropical climate system is investigated using both an ocean circulation model and a fully-coupled ocean–atmosphere circulation model, which interact with a biogeochemical model. We found that the presence of chlorophyll can have significant impact on the characteristics of the El Niño-Southern Oscillation (ENSO), including its amplitude and asymmetry, as well as on the mean state. That is, chlorophyll generally increases mean sea surface temperature (SST) due to the direct biological heating. However, SST in the eastern equatorial Pacific decreases due to the stronger indirect dynamical response to the biological effects outweighing the direct thermal response. It is demonstrated that this biologically-induced SST cooling is intensified and conveyed to other tropical-ocean basins when atmosphere–ocean coupling is taken into account. It is also found that the presence of chlorophyll affects the magnitude of ENSO by two different mechanisms; one is an amplifying effect by the mean chlorophyll, which is associated with shoaling of the mean thermocline depth, and the other is a damping effect derived from the interactively-varying chlorophyll coupled with the physical model. The atmosphere–ocean coupling reduces the biologically-induced ENSO amplifying effect through the weakening of atmospheric feedback. Lastly, there is also a biological impact on ENSO which enhances the positive skewness. This skewness change is presumably caused by the phase dependency of thermocline feedback which affects the ENSO magnitude.     

Quantifying two-way influences between the Arctic and mid-latitudes through regionally increased CO2 concentrations in coupled climate simulations

Climate Dynamics - In which direction is the influence larger: from the Arctic to the mid-latitudes or vice versa? To answer this question, CO2 concentrations have been regionally increased in...     

Understanding the links between climate feedbacks,variability and change using a two-layer energy balance model

Climate Dynamics - A simple, two-layer energy balance model (EBM) is used to investigate climate variability in Coupled Model Intercomparison Project Phase 5 (CMIP5) models and examine possible...     

Simulated climate change during the last 1,000 years: comparing the ECHO-G general circulation model with the MAGICC simple climate model

An intercomparison of eight climate simulations, each driven with estimated natural and anthropogenic forcings for the last millennium, indicates that the so-called “Erik” simulation of the ECHO-G coupled ocean-atmosphere climate model exhibits atypical behaviour. The ECHO-G simulation has a much stronger cooling trend from 1000 to 1700 and a higher rate of warming since 1800 than the other simulations, with the result that the overall amplitude of millennial-scale temperature variations in the ECHO-G simulation is much greater than in the other models. The MAGICC (Model for the Assessment of Greenhouse-gas-Induced Climate Change) simple climate model is used to investigate possible causes of this atypical behaviour. It is shown that disequilibrium in the initial conditions probably contributes spuriously to the cooling trend in the early centuries of the simulation, and that the omission of tropospheric sulphate aerosol forcing is the likely explanation for the anomalously large recent warming. The simple climate model results are used to adjust the ECHO-G Erik simulation to mitigate these effects, which brings the simulation into better agreement with the other seven models considered here and greatly reduces the overall range of temperature variations during the last millennium simulated by ECHO-G. Smaller inter-model differences remain which can probably be explained by a combination of the particular forcing histories and model sensitivities of each experiment. These have not been investigated here, though we have diagnosed the effective climate sensitivity of ECHO-G to be 2.39±0.11 K for a doubling of CO₂.     

On the role of domain size and resolution in the simulations with the HIRHAM region climate model

We investigate the simulated temperature and precipitation of the HIRHAM regional climate model using systematic variations in domain size, resolution and detailed location in a total of eight simulations. HIRHAM was forced by ERA-Interim boundary data and the simulations focused on higher resolutions in the range of 5.5–12 km. HIRHAM outputs of seasonal precipitation and temperature were assessed by calculating distributed model errors against a higher resolution data set covering Denmark and a 0.25° resolution data set covering Europe. Furthermore the simulations were statistically tested against the Danish data set using bootstrap statistics. The results from the distributed validation of precipitation showed lower errors for the winter (DJF) season compared to the spring (MAM), fall (SON) and, in particular, summer (JJA) seasons for both validation data sets. For temperature, the pattern was in the opposite direction, with the lowest errors occurring for the JJA season. These seasonal patterns between precipitation and temperature are seen in the bootstrap analysis. It also showed that using a 4,000 × 2,800 km simulation with an 11 km resolution produced the highest significance levels. Also, the temperature errors were more highly significant than precipitation. In similarly sized domains, 12 of 16 combinations of variables, observation validation data and seasons showed better results for the highest resolution domain, but generally the most significant improvements were seen when varying the domain size.