Changes in aerosols cause a change in net top-of-the-atmosphere
(ToA) short-wave and long-wave radiative fluxes; rapid adjustments in clouds,
water vapour and temperature; and an effective radiative ...forcing (ERF)
of the planetary energy budget. The diverse sources of model uncertainty and
the computational cost of running climate models make it difficult to isolate
the main causes of aerosol ERF uncertainty and to understand how observations
can be used to constrain it. We explore the aerosol ERF uncertainty by using
fast model emulators to generate a very large set of aerosol–climate model
variants that span the model uncertainty due to 27 parameters
related to atmospheric and aerosol processes. Sensitivity analyses shows that
the uncertainty in the ToA flux is dominated (around 80 %) by uncertainties
in the physical atmosphere model, particularly parameters that affect cloud
reflectivity. However, uncertainty in the change in ToA flux caused by
aerosol emissions over the industrial period (the aerosol ERF) is controlled
by a combination of uncertainties in aerosol (around 60 %) and physical
atmosphere (around 40 %) parameters. Four atmospheric and aerosol parameters
account for around 80 % of the uncertainty in short-wave ToA flux (mostly
parameters that directly scale cloud reflectivity, cloud water content or
cloud droplet concentrations), and these parameters also account for around
60 % of the aerosol ERF uncertainty. The common causes of uncertainty mean
that constraining the modelled planetary brightness to tightly match
satellite observations changes the lower 95 % credible aerosol ERF value from
−2.65 to −2.37 W m−2. This
suggests the strongest forcings (below around −2.4 W m−2)
are inconsistent with observations. These results show that, regardless of
the fact that the ToA flux is 2 orders of magnitude larger than the aerosol
ERF, the observed flux can constrain the uncertainty in ERF because their
values are connected by constrainable process parameters. The key to reducing
the aerosol ERF uncertainty further will be to identify observations that can
additionally constrain individual parameter ranges and/or combined parameter
effects, which can be achieved through sensitivity analysis of perturbed
parameter ensembles.
In addition to influencing climatic conditions directly through radiative forcing, increasing carbon dioxide concentration influences the climate system through its effects on plant physiology. Plant ...stomata generally open less widely under increased carbon dioxide concentration, which reduces transpiration and thus leaves more water at the land surface. This driver of change in the climate system, which we term 'physiological forcing', has been detected in observational records of increasing average continental runoff over the twentieth century. Here we use an ensemble of experiments with a global climate model that includes a vegetation component to assess the contribution of physiological forcing to future changes in continental runoff, in the context of uncertainties in future precipitation. We find that the physiological effect of doubled carbon dioxide concentrations on plant transpiration increases simulated global mean runoff by 6 per cent relative to pre-industrial levels; an increase that is comparable to that simulated in response to radiatively forced climate change (11± 6 per cent). Assessments of the effect of increasing carbon dioxide concentrations on the hydrological cycle that only consider radiative forcing will therefore tend to underestimate future increases in runoff and overestimate decreases. This suggests that freshwater resources may be less limited than previously assumed under scenarios of future global warming, although there is still an increased risk of drought. Moreover, our results highlight that the practice of assessing the climate-forcing potential of all greenhouse gases in terms of their radiative forcing potential relative to carbon dioxide does not accurately reflect the relative effects of different greenhouse gases on freshwater resources.
The effect of observational constraint on the ranges of uncertain physical and chemical process parameters was explored in a global aerosol–climate model. The study uses 1 million variants of the ...Hadley Centre General Environment Model version 3 (HadGEM3) that sample 26 sources of uncertainty, together with over 9000 monthly aggregated grid-box measurements of aerosol optical depth, PM2.5, particle number concentrations, sulfate and organic mass concentrations. Despite many compensating effects in the model, the procedure constrains the probability distributions of parameters related to secondary organic aerosol, anthropogenic SO2 emissions, residential emissions, sea spray emissions, dry deposition rates of SO2 and aerosols, new particle formation, cloud droplet pH and the diameter of primary combustion particles. Observational constraint rules out nearly 98 % of the model variants. On constraint, the ±1σ (standard deviation) range of global annual mean direct radiative forcing (RFari) is reduced by 33 % to −0.14 to −0.26 W m−2, and the 95 % credible interval (CI) is reduced by 34 % to −0.1 to −0.32 W m−2. For the global annual mean aerosol–cloud radiative forcing, RFaci, the ±1σ range is reduced by 7 % to −1.66 to −2.48 W m−2, and the 95 % CI by 6 % to −1.28 to −2.88 W m−2. The tightness of the constraint is limited by parameter cancellation effects (model equifinality) as well as the large and poorly defined “representativeness error” associated with comparing point measurements with a global model. The constraint could also be narrowed if model structural errors that prevent simultaneous agreement with different measurement types in multiple locations and seasons could be improved. For example, constraints using either sulfate or PM2.5 measurements individually result in RFari±1σ ranges that only just overlap, which shows that emergent constraints based on one measurement type may be overconfident.
Global and local feedback analysis techniques have been applied to two ensembles of mixed layer equilibrium CO.sub.2 doubling climate change experiments, from the CFMIP (Cloud Feedback Model ...Intercomparison Project) and QUMP (Quantifying Uncertainty in Model Predictions) projects. Neither of these new ensembles shows evidence of a statistically significant change in the ensemble mean or variance in global mean climate sensitivity when compared with the results from the mixed layer models quoted in the Third Assessment Report of the IPCC. Global mean feedback analysis of these two ensembles confirms the large contribution made by inter-model differences in cloud feedbacks to those in climate sensitivity in earlier studies; net cloud feedbacks are responsible for 66% of the inter-model variance in the total feedback in the CFMIP ensemble and 85% in the QUMP ensemble. The ensemble mean global feedback components are all statistically indistinguishable between the two ensembles, except for the clear-sky shortwave feedback which is stronger in the CFMIP ensemble. While ensemble variances of the shortwave cloud feedback and both clear-sky feedback terms are larger in CFMIP, there is considerable overlap in the cloud feedback ranges; QUMP spans 80% or more of the CFMIP ranges in longwave and shortwave cloud feedback. We introduce a local cloud feedback classification system which distinguishes different types of cloud feedbacks on the basis of the relative strengths of their longwave and shortwave components, and interpret these in terms of responses of different cloud types diagnosed by the International Satellite Cloud Climatology Project simulator. In the CFMIP ensemble, areas where low-top cloud changes constitute the largest cloud response are responsible for 59% of the contribution from cloud feedback to the variance in the total feedback. A similar figure is found for the QUMP ensemble. Areas of positive low cloud feedback (associated with reductions in low level cloud amount) contribute most to this figure in the CFMIP ensemble, while areas of negative cloud feedback (associated with increases in low level cloud amount and optical thickness) contribute most in QUMP. Classes associated with high-top cloud feedbacks are responsible for 33 and 20% of the cloud feedback contribution in CFMIP and QUMP, respectively, while classes where no particular cloud type stands out are responsible for 8 and 21%.
Aerosol radiative forcing uncertainty affects estimates of climate sensitivity and limits model skill in terms of making climate projections. Efforts to
improve the representations of physical ...processes in climate models, including extensive comparisons with observations, have not significantly
constrained the range of possible aerosol forcing values. A far stronger constraint, in particular for the lower (most-negative) bound, can be
achieved using global mean energy balance arguments based on observed changes in historical temperature. Here, we show that structural deficiencies in a climate model, revealed as inconsistencies among observationally constrained cloud properties in the model, limit the effectiveness of observational constraint of the uncertain physical processes. We sample the uncertainty in 37 model parameters related to aerosols, clouds, and radiation in a perturbed parameter ensemble of the UK Earth System Model and evaluate 1 million model variants (different parameter settings from Gaussian
process emulators) against satellite-derived observations over several cloudy regions. Our analysis of a very large set of model variants exposes
model internal inconsistencies that would not be apparent in a small set of model simulations, of an order that may be evaluated during model-tuning efforts. Incorporating observations associated with these inconsistencies weakens any forcing constraint because they require a wider range of parameter values to accommodate conflicting information. We show that, by neglecting variables associated with these inconsistencies, it is possible to reduce the parametric uncertainty in global mean aerosol forcing by more than 50 %, constraining it to a range (around −1.3 to −0.1 W m−2) in close agreement with energy balance constraints. Our estimated aerosol forcing range is the maximum feasible constraint using our structurally imperfect model and the chosen observations. Structural model developments targeted at the identified inconsistencies would enable a larger set of observations to be used for constraint, which would then very likely narrow the uncertainty further and possibly alter the central estimate. Such an approach provides a rigorous pathway to improved model realism and reduced uncertainty that has so far not been achieved through the normal model development approach.
Comprehensive global climate models are the only tools that account for the complex set of processes which will determine future climate change at both a global and regional level. Planners are ...typically faced with a wide range of predicted changes from different models of unknown relative quality, owing to large but unquantified uncertainties in the modelling process. Here we report a systematic attempt to determine the range of climate changes consistent with these uncertainties, based on a 53-member ensemble of model versions constructed by varying model parameters. We estimate a probability density function for the sensitivity of climate to a doubling of atmospheric carbon dioxide levels, and obtain a 5-95 per cent probability range of 2.4-5.4 °C. Our probability density function is constrained by objective estimates of the relative reliability of different model versions, the choice of model parameters that are varied and their uncertainty ranges, specified on the basis of expert advice. Our ensemble produces a range of regional changes much wider than indicated by traditional methods based on scaling the response patterns of an individual simulation.
The main aim of this two-part study is to use a perturbed parameter ensemble (PPE) to select plausible and diverse variants of a relatively expensive climate model for use in climate projections. In ...this first part, the extent to which climate biases develop at weather forecast timescales is assessed with two PPEs, which are based on 5-day forecasts and 10-year simulations with a relatively coarse resolution (N96) atmosphere-only model. Both ensembles share common parameter combinations and strong emergent relationships are found for a wide range of variables between the errors on two timescales. These relationships between the PPEs are demonstrated at several spatial scales from global (using mean square errors), to regional (using pattern correlations), and to individual grid boxes where a large fraction of them show positive correlations. The study confirms more robustly than in previous studies that investigating the errors on weather timescales provides an affordable way to identify and filter out model variants that perform poorly at short timescales and are likely to perform poorly at longer timescales too. The use of PPEs also provides additional information for model development, by identifying parameters and processes responsible for model errors at the two different timescales, and systematic errors that cannot be removed by any combination of parameter values.
Observational constraint of simulated aerosol and cloud
properties is an essential part of building trustworthy climate models for
calculating aerosol radiative forcing. Models are usually tuned to ...achieve
good agreement with observations, but tuning produces just one of many
potential variants of a model, so the model uncertainty cannot be determined.
Here we estimate the uncertainty in aerosol effective radiative forcing (ERF)
in a tuned climate model by constraining 4 million variants of the
HadGEM3-UKCA aerosol–climate model to match nine common observations
(top-of-atmosphere shortwave flux, aerosol optical depth, PM2.5, cloud
condensation nuclei at 0.2 % supersaturation (CCN0.2), and
concentrations of sulfate, black carbon and organic carbon, as well as
decadal trends in aerosol optical depth and surface shortwave radiation.) The
model uncertainty is calculated by using a perturbed parameter ensemble that
samples 27 uncertainties in both the aerosol model and the physical climate
model, and we use synthetic observations generated from the model itself to
determine the potential of each observational type to constrain this
uncertainty. Focusing over Europe in July,
we show that the aerosol ERF uncertainty can be reduced by about 30 % by
constraining it to the nine observations, demonstrating that producing
climate models with an observationally plausible “base state” can
contribute to narrowing the uncertainty in aerosol ERF. However, the
uncertainty in the aerosol ERF after observational constraint is large
compared to the typical spread of a multi-model ensemble. Our results
therefore raise questions about whether the underlying multi-model
uncertainty would be larger if similar approaches as adopted here were
applied more widely. The approach presented in this study could be used to
identify the most effective observations for model constraint. It is hoped
that aerosol ERF uncertainty can be further reduced by introducing
process-related constraints; however, any such results will be robust only if
the enormous number of potential model variants is explored.
A new coupled general circulation climate model developed at the Met Office’s Hadley Centre is presented, and aspects of its performance in climate simulations run for the Intergovernmental Panel on ...Climate Change Fourth Assessment Report (IPCC AR4) documented with reference to previous models. The Hadley Centre Global Environmental Model version 1 (HadGEM1) is built around a new atmospheric dynamical core; uses higher resolution than the previous Hadley Centre model, HadCM3; and contains several improvements in its formulation including interactive atmospheric aerosols (sulphate, black carbon, biomass burning, and sea salt) plus their direct and indirect effects. The ocean component also has higher resolution and incorporates a sea ice component more advanced than HadCM3 in terms of both dynamics and thermodynamics. HadGEM1 thus permits experiments including some interactive processes not feasible with HadCM3. The simulation of present-day mean climate in HadGEM1 is significantly better overall in comparison to HadCM3, although some deficiencies exist in the simulation of tropical climate and El Niño variability. We quantify the overall improvement using a quasi-objective climate index encompassing a range of atmospheric, oceanic, and sea ice variables. It arises partly from higher resolution but also from greater fidelity in modeling dynamical and physical processes, for example, in the representation of clouds and sea ice. HadGEM1 has a similar effective climate sensitivity (2.8 K) to a CO₂ doubling as HadCM3 (3.1 K), although there are significant regional differences in their response patterns, especially in the Tropics. HadGEM1 is anticipated to be used as the basis both for higher-resolution and higher-complexity Earth System studies in the near future.
A methodology is described for probabilistic predictions of future climate. This is based on a set of ensemble simulations of equilibrium and time-dependent changes, carried out by perturbing poorly ...constrained parameters controlling key physical and biogeochemical processes in the HadCM3 coupled ocean-atmosphere global climate model. These (ongoing) experiments allow quantification of the effects of earth system modelling uncertainties and internal climate variability on feedbacks likely to exert a significant influence on twenty-first century climate at large regional scales. A further ensemble of regional climate simulations at 25 km resolution is being produced for Europe, allowing the specification of probabilistic predictions at spatial scales required for studies of climate impacts. The ensemble simulations are processed using a set of statistical procedures, the centrepiece of which is a Bayesian statistical framework designed for use with complex but imperfect models. This supports the generation of probabilities constrained by a wide range of observational metrics, and also by expert-specified prior distributions defining the model parameter space. The Bayesian framework also accounts for additional uncertainty introduced by structural modelling errors, which are estimated using our ensembles to predict the results of alternative climate models containing different structural assumptions. This facilitates the generation of probabilistic predictions combining information from perturbed physics and multi-model ensemble simulations. The methodology makes extensive use of emulation and scaling techniques trained on climate model results. These are used to sample the equilibrium response to doubled carbon dioxide at any required point in the parameter space of surface and atmospheric processes, to sample time-dependent changes by combining this information with ensembles sampling uncertainties in the transient response of a wider set of earth system processes, and to sample changes at local scales. The methodology is necessarily dependent on a number of expert choices, which are highlighted throughout the paper.
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