Simple Climate Model Research Articles

Bayesian estimation of climate sensitivity based on a simple climate model fitted to observations of hemispheric temperatures and global ocean heat content

Predictions of climate change are uncertain mainly because of uncertainties in the emissions of greenhouse gases and how sensitive the climate is to changes in the abundance of the atmospheric constituents. The equilibrium climate sensitivity is defined as the temperature increase because of a doubling of the CO2 concentration in the atmosphere when the climate reaches a new steady state. CO2 is only one out of the several external factors that affect the global temperature, called radiative forcing mechanisms as a collective term. In this paper, we present a model framework for estimating the climate sensitivity. The core of the model is a simple, deterministic climate model based on elementary physical laws such as energy balance. It models yearly hemispheric surface temperature and global ocean heat content as a function of historical radiative forcing. This deterministic model is combined with an empirical, stochastic model and fitted to observations on global temperature and ocean heat content, conditioned on estimates of historical radiative forcing. We use a Bayesian framework, with informative priors on a subset of the parameters and flat priors on the climate sensitivity and the remaining parameters. The model is estimated by Markov Chain Monte Carlo techniques. Copyright © 2012 John Wiley & Sons, Ltd.

Is it possible to limit global warming to no more than 1.5°C?

This study explores the feasibility of limiting increases in global temperature to 1.5°C above pre-industrial levels. A probabilistic simple climate model is used to identify emissions paths that offer at least a 50% chance of achieving this goal. We conclude that it is more likely than not that warming would exceed 1.5°C, at least temporarily, under plausible mitigation scenarios. We have identified three criteria of emissions paths that could meet the 1.5°C goal with a temporary overshoot of no more than 50 years: early and strong reductions in emissions, with global emissions peaking in 2015 and falling to at most 44-48 GtCO 2e in 2020; rapid reductions in annual global emissions after 2020 (of at least 3-4% per year); very low annual global emissions by 2100 (less than 2-4 GtCO 2e) and falling to zero (or below) in the 22nd century. The feasibility of these characteristics is uncertain. We conclude that the proposed date of review of the 1.5°C goal, set at 2015, may be too late to achieve the necessary scaling up of emissions cuts to achieve this goal.

Carbon capture and storage deployment rates: needs and feasibility

Carbon capture and storage (CCS) may become a key technology to limit human-induced global warming, but many uncertainties prevail, including the necessary technological development, costs, legal ramifications, and siting. As such, an important question is the scale of carbon dioxide abatement we require from CCS to meet future climate targets, and whether they appear reasonable. For a number of energy technology and efficiency improvement scenarios, we use a simple climate model to assess the necessary contribution from CCS to ‘fill the gap’ between scenarios’ carbon dioxide emissions levels and the levels needed to meet alternative climate targets. The need for CCS depends on early or delayed action to curb emissions and the characteristics of the assumed energy scenario. To meet a 2.5°C target a large contribution and fast deployment rates for CCS are required. The required deployment rates are much faster than those seen in the deployment of renewable energy technologies as well as nuclear power the last decades, and may not be feasible. This indicates that more contributions are needed from other low-carbon energy technologies and improved energy efficiency, or substitution of coal for gas in the first half of the century. In addition the limited availability of coal and gas by end of the century and resulting limited scope for CCS implies that meeting the 2.5°C target would require significant contributions from one or more of the following options: CCS linked to oil use, biomass energy based CCS (BECCS), and CCS linked to industrial processes.

Vertical and horizontal processes in the global atmosphere and the maximum entropy production conjecture

Abstract. The objective of this paper is to reconsider the Maximum Entropy Production conjecture (MEP) in the context of a very simple two-dimensional zonal-vertical climate model able to represent the total material entropy production due at the same time to both horizontal and vertical heat fluxes. MEP is applied first to a simple four-box model of climate which accounts for both horizontal and vertical material heat fluxes. It is shown that, under condition of fixed insolation, a MEP solution is found with reasonably realistic temperature and heat fluxes, thus generalising results from independent two-box horizontal or vertical models. It is also shown that the meridional and the vertical entropy production terms are independently involved in the maximisation and thus MEP can be applied to each subsystem with fixed boundary conditions. We then extend the four-box model by increasing its resolution, and compare it with GCM output. A MEP solution is found which is fairly realistic as far as the horizontal large scale organisation of the climate is concerned whereas the vertical structure looks to be unrealistic and presents seriously unstable features. This study suggest that the thermal meridional structure of the atmosphere is predicted fairly well by MEP once the insolation is given but the vertical structure of the atmosphere cannot be predicted satisfactorily by MEP unless constraints are imposed to represent the determination of longwave absorption by water vapour and clouds as a function of the state of the climate. Furthermore an order-of-magnitude estimate of contributions to the material entropy production due to horizontal and vertical processes within the climate system is provided by using two different methods. In both cases we found that approximately 40 mW m−2 K−1 of material entropy production is due to vertical heat transport and 5–7 mW m−2 K−1 to horizontal heat transport.

Bayesian Learning of Climate Sensitivity I: Synthetic Observations

The instrumental temperature records are affected by both external climate forcings—in particular, the increase of long-lived greenhouse gas emissions—and natural, internal variability. Estimates of the value of equilibrium climate sensitivity—the change in global-mean equilibrium near-surface temperature due to a doubling of the pre-industrial CO2 concentration—and other climate parameters using these observational records are affected by the presence of the internal variability. A different realization of the natural variability will result in different estimates of the values of these climate parameters. In this study we apply Bayesian estimation to simulated temperature and ocean heat-uptake records generated by our Climate Research Group’s Simple Climate Model for known values of equilibrium climate sensitivity, T2x direct sulfate aerosol forcing in reference year 2000, FASA, and oceanic heat diffusivity, ΔT2x. We choose the simulated records for one choice of values of the climate parameters to serve as the synthetic observations. To each of the simulated temperature records we add a number of draws of the quasi-periodic oscillations and stochastic noise, determined from the observed temperature record. For cases considering only values of ΔT2x and/or κ, the Bayesian estimation converges to the value(s) of ΔT2x and/or κ used to generate the synthetic observations. However, for cases studying FASA, the Bayesian analysis does not converge to the “true” value used to generate the synthetic observations. We show that this is a problem of low signal-to-noise ratio: by substituting an artificial, continuously increasing sulfate record, we greatly improve the value obtained through Bayesian estimation. Our results indicate Bayesian learning techniques will be useful tools in constraining the values of ΔT2x and κ but not FASA In our Group’s future work we will extend the methods used here to the observed, instrumental records of global-mean temperature increase and ocean heat uptake.

Communicating the Probabilities of Extreme Surface Temperature Outcomes

The magnitude of the future global warming is uncertain, but the possible dramatic changes associated with high temperatures have seen rising attention in the literature. Projections of temperature change in the literature are often presented in probabilistic terms and typically highlight the most likely ranges of future temperature under assumed emission scenarios. However, focusing on these high probability outcomes of global warming omits important information related to the threats of low-probability but high-impact outcomes under more extreme change. As such, we argue that the literature should place more emphasis on communicating the probabilities of extreme temperature change, in a way that is accessible to policymakers and the general public. The damage associated with climate change is likely to be non-linear with temperature, and thus extreme temperature changes may pose a larger risk than the most likely outcomes. We use a simple climate model to explore the probabilities of high surface temperature under business as usual emissions scenarios, given current knowledge of the climate system. In a business as usual scenario (A1FI) we find the probability of “likely” warming (central 66%) to be approximately 4.4°C-6.9°C in 2100 (above 1900 levels). However, we find extreme (>7°C) warming to embody a notable portion of damage risk compared to this likely range.

Correlation Models for Temperature Fields

Abstract This paper presents derivations of some analytical forms for spatial correlations of evolving random fields governed by a white-noise-driven damped diffusion equation that is the analog of autoregressive order 1 in time and autoregressive order 2 in space. The study considers the two-dimensional plane and the surface of a sphere, both of which have been studied before, but here time is introduced to the problem. Such models have a finite characteristic length (roughly the separation at which the autocorrelation falls to 1/e) and a relaxation time scale. In particular, the characteristic length of a particular temporal Fourier component of the field increases to a finite value as the frequency of the particular component decreases. Some near-analytical formulas are provided for the results. A potential application is to the correlation structure of surface temperature fields and to the estimation of large area averages, depending on how the original datastream is filtered into a distribution of Fourier frequencies (e.g., moving average, low pass, or narrow band). The form of the governing equation is just that of the simple energy balance climate models, which have a long history in climate studies. The physical motivation provided by the derivation from a climate model provides some heuristic appeal to the approach and suggests extensions of the work to nonuniform cases.

Radiative Forcing Impacts of Boreal Forest Biofuels: A Scenario Study for Norway in Light of Albedo

Radiative forcing impacts due to increased harvesting of boreal forests for use as transportation biofuel in Norway are quantified using simple climate models together with life cycle emission data, MODIS surface albedo data, and a dynamic land use model tracking carbon flux and clear-cut area changes within productive forests over a 100-year management period. We approximate the magnitude of radiative forcing due to albedo changes and compare it to the forcing due to changes in the carbon cycle for purposes of attributing the net result, along with changes in fossil fuel emissions, to the combined anthropogenic land use plus transport fuel system. Depending on albedo uncertainty and uncertainty about the geographic distribution of future logging activity, we report a range of results, thus only general conclusions about the magnitude of the carbon offset potential due to changes in surface albedo can be drawn. Nevertheless, our results have important implications for how forests might be managed for mitigating climate change in light of this additional biophysical criterion, and in particular, on future biofuel policies throughout the region. Future research efforts should be directed at understanding the relationships between the physical properties of managed forests and albedo, and how albedo changes in time as a result of specific management interventions.

Water storage change in the Himalayas from the Gravity Recovery and Climate Experiment (GRACE) and an empirical climate model

The Himalayas and Tibetan Plateau harbor hundreds of mountain lakes along with thousands of glaciers and high‐elevation snowfields. This is the source of water for the upper reaches of Asia's main river systems, providing the livelihood for millions of people in the subregion. Climate change is therefore critical for the Himalaya snow and glacier hydrology, the dependent ecosystems, and the people. Whereas temperature and precipitation are common indicators for climate change, snow and glacier dynamics are reliable precursors of a warming or cooling climate. This study uses a simple empirical climate model (ECM) and the Gravity Recovery and Climate Experiment (GRACE) satellite data to analyze water storage dynamics in the 5.072 × 106 km2 Himalayas and Tibetan Plateau region. About 72 consecutive months (January 2003 through December 2008) of data are used in the study. The temperature and precipitation (snow plus rain) data are acquired from the Global Land Data Assimilation System (GLDAS) Noah land surface model and are validated with ground truth data from 205 meteorological stations. Total water storage change derived from the GRACE gravity data is fitted with a simple sinusoidal least squares regression model. A favorable agreement exists between the GRACE and sinusoidal curve (R2 = 0.81 and root‐mean‐square error (RMSE) = 8.73 mm), suggesting that random errors in GRACE data are small. However, the sinusoidal fit does not quantify systematic errors in GRACE data. Agreements between the GRACE‐ and ECM‐estimated storage changes are also favorable at both monthly (R2 = 0.93, RMSE = 5.46 mm) and seasonal (R2 = 0.83, RMSE = 7.64 mm) cycles. The agreements (significant at p < 0.01) indicate not only GRACE's ability to detect storage signal but also that of the ECM model to characterize storage change in the snow and glacier hydrology. There is clear seasonality in the storage anomaly, with the highest in summer and lowest in winter. The corresponding storage change is delayed by a quarter of the year. The GRACE and ECM model indicate an overall negative storage trend of 0.36 ± 0.03 mm/month or 21.91 ± 1.95 km3/yr for the study area (significant at p < 0.1). Given that snow and glaciers are particularly sensitive to temperature change, the negative storage trend could be indicative of warming climate conditions in the region. Groundwater abstraction (mainly for irrigation) in the southern plains, coupled with dwindling snowfall in the northern massifs, is a critical storage loss factor in the region. Invariably, storage loss in the Himalayan‐Tibetan Plateau region could have negative implications for the hydrology, dependent ecosystems, and livelihoods of millions of people.

Impacts of ocean gateway and basin width on Tertiary tropical climate variability in a prototype model

Using a simple tropical climate model, we investigated possible impacts of changes in oceanic seaways (Panama and Tethys) and ocean basin sizes (great Pacific and narrow Atlantic) on tropical climate variability during Tertiary. Our model showed that the opening of seaways had little influence on climate variability in the tropical Pacific because the climate variability in the Pacific Ocean’s large basins were internally generated, regardless of the variation in the tropical Atlantic Ocean. Conversely, the climate variability in the tropical Atlantic Ocean was highly dependent on the tropical Pacific Ocean; thus, an opening seaway, particularly the Panama seaway, was crucial in generating the interannual variability in the tropical Atlantic Ocean. We also found that in the Pacific Ocean, basin size strongly modified the period and amplitude of the interannual variability of both the Pacific and Atlantic Oceans due to ocean wave dynamics.

Habitable Zone Limits for Dry Planets

Most discussion of habitable planets has focused on Earth-like planets with globally abundant liquid water. For an "aqua planet" like Earth, the surface freezes if far from its sun, and the water vapor greenhouse effect runs away if too close. Here we show that "land planets" (desert worlds with limited surface water) have wider habitable zones than aqua planets. For planets at the inner edge of the habitable zone, a land planet has two advantages over an aqua planet: (i) the tropics can emit longwave radiation at rates above the traditional runaway limit because the air is unsaturated and (ii) the dry air creates a dry stratosphere that limits hydrogen escape. At the outer limits of the habitable zone, the land planet better resists global freezing because there is less water for clouds, snow, and ice. Here we describe a series of numerical experiments using a simple three-dimensional global climate model for Earth-sized planets. Other things (CO(2), rotation rate, surface pressure) unchanged, we found that liquid water remains stable at the poles of a low-obliquity land planet until net insolation exceeds 415 W/m(2) (170% that of modern Earth), compared to 330 W/m(2) (135%) for the aqua planet. At the outer limits, we found that a low-obliquity land planet freezes at 77%, while the aqua planet freezes at 90%. High-obliquity land and aqua planets freeze at 58% and 72%, respectively, with the poles offering the last refuge. We show that it is possible that, as the Sun brightens, an aqua planet like Earth can lose most of its hydrogen and become a land planet without first passing through a sterilizing runaway greenhouse. It is possible that Venus was a habitable land planet as recently as 1 billion years ago.

Global Warming and Local Dimming: The Statistical Evidence

Two effects largely determine global warming: the well-known greenhouse effect and the less well-known solar radiation effect. An increase in concentrations of carbon dioxide and other greenhouse gases contributes to global warming: the greenhouse effect. In addition, small particles, called aerosols, reflect and absorb sunlight in the atmosphere. More pollution causes an increase in aerosols, so that less sunlight reaches the Earth (global dimming). Despite its name, global dimming is primarily a local (or regional) effect. Because of the dimming the Earth becomes cooler: the solar radiation effect. Global warming thus consists of two components: the (global) greenhouse effect and the (local) solar radiation effect, which work in opposite directions. Only the sum of the greenhouse effect and the solar radiation effect is observed, not the two effects separately. Our purpose is to identify the two effects. This is important, because the existence of the solar radiation effect obscures the magnitude of the greenhouse effect. We propose a simple climate model with a small number of parameters. We gather data from a large number of weather stations around the world for the period 1959–2002. We then estimate the parameters using dynamic panel data methods, and quantify the parameter uncertainty. Next, we decompose the estimated temperature change of 0.73○C (averaged over the weather stations) into a greenhouse effect of 1.87○C, a solar radiation effect of −1.09○C, and a small remainder term. Finally, we subject our findings to extensive sensitivity analyses.

On the Persistence of Cold-Season SST Anomalies Associated with the Annular Modes

Abstract In this study, a simple stochastic climate model is used to examine the impact of the ocean mixed layer depth, surface turbulent energy fluxes, and Ekman currents on the persistence of cold-season extratropical sea surface temperature (SST) anomalies associated with variability in the annular modes of atmospheric circulation in both hemispheres. Observational analysis reveals that during the cold season, SST anomalies associated with the southern annular mode (SSTSAM) persist considerably longer than those associated with the northern annular mode (SSTNAM). Using the simple model, it is shown that the persistence of the cold-season SSTSAM is consistent with the simple stochastic climate paradigm in which the atmospheric forcing is approximated as white noise, and the persistence of SST anomalies can be largely determined by the thermal inertia of the ocean mixed layer. In the North Atlantic, however, the simple climate model overestimates the persistence of the cold-season SSTNAM. It is thought that this overestimate occurs because the NAM-related heat flux forcing cannot be described purely as white noise but must also include a feedback from the underlying SST anomalies.

Bistability of the Atlantic overturning circulation in a global climate model and links to ocean freshwater transport

[1] The possibility of a rapid collapse in the strength of the Atlantic meridional overturning circulation (AMOC), with associated impacts on climate, has long been recognized. The suggested basis for this risk is the existence of two stable regimes of the AMOC (‘on’ and ‘off’), and such bistable behaviour has been identified in a range of simplified climate models. However, up to now, no state-of-the-art atmosphere-ocean coupled global climate model (AOGCM) has exhibited such behaviour, leading to the interpretation that the AMOC is more stable than simpler models indicate. Here we demonstrate AMOC bistability in the response to freshwater perturbations in the FAMOUS AOGCM - the most complex AOGCM to exhibit such behaviour to date. The results also support recent suggestions that the direction of the net freshwater transport at the southern boundary of the Atlantic by the AMOC may be a useful physical indicator of the existence of bistability. We also present new estimates for this net freshwater transport by the AMOC from a range of ocean reanalyses which suggest that the Atlantic AMOC is currently in a bistable regime, although with large uncertainties. More accurate observational constraints, and an improved physical understanding of this quantity, could help narrow uncertainty in the future evolution of the AMOC and to assess the risk of a rapid AMOC collapse.

Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 – Part 1: Model description and calibration

Abstract. Current scientific knowledge on the future response of the climate system to human-induced perturbations is comprehensively captured by various model intercomparison efforts. In the preparation of the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), intercomparisons were organized for atmosphere-ocean general circulation models (AOGCMs) and carbon cycle models, named "CMIP3" and "C4MIP", respectively. Despite their tremendous value for the scientific community and policy makers alike, there are some difficulties in interpreting the results. For example, radiative forcings were not standardized across the various AOGCM integrations and carbon cycle runs, and, in some models, key forcings were omitted. Furthermore, the AOGCM analysis of plausible emissions pathways was restricted to only three SRES scenarios. This study attempts to address these issues. We present an updated version of MAGICC, the simple carbon cycle-climate model used in past IPCC Assessment Reports with enhanced representation of time-varying climate sensitivities, carbon cycle feedbacks, aerosol forcings and ocean heat uptake characteristics. This new version, MAGICC6, is successfully calibrated against the higher complexity AOGCMs and carbon cycle models. Parameterizations of MAGICC6 are provided. The mean of the emulations presented here using MAGICC6 deviates from the mean AOGCM responses by only 2.2% on average for the SRES scenarios. This enhanced emulation skill in comparison to previous calibrations is primarily due to: making a "like-with-like comparison" using AOGCM-specific subsets of forcings; employing a new calibration procedure; as well as the fact that the updated simple climate model can now successfully emulate some of the climate-state dependent effective climate sensitivities of AOGCMs. The diagnosed effective climate sensitivity at the time of CO2 doubling for the AOGCMs is on average 2.88 °C, about 0.33 °C cooler than the mean of the reported slab ocean climate sensitivities. In the companion paper (Part 2) of this study, we examine the combined climate system and carbon cycle emulations for the complete range of IPCC SRES emissions scenarios and the new RCP pathways.

A parametric sensitivity study of entropy production and kinetic energy dissipation using the FAMOUS AOGCM

The possibility of applying either the maximum entropy production conjecture of Paltridge (Q J R Meteorol Soc 101:475–484, 1975) or the conjecture of Lorenz (Generation of available potential energy and the intensity of the general circulation. Pergamon, Tarrytown, 1960) of maximum generation of available potential energy (APE) in FAMOUS, a complex but low-resolution AOGCM, is explored by varying some model parameters to which the simulated climate is highly sensitive, particularly the convective entrainment rate, $$\epsilon$$ , and cloud droplet-to-rain-conversion rate, c T . The climate response is analysed in terms of its entropy production and the strength of the Lorenz energy cycle. If either conjecture is true, the parameter values which yield the most realistic climate will also maximise the relevant quantity. No maximum is found in the total material entropy production, which is dominated by the hydrological cycle and tends to increase monotonically with global-mean temperature, which is not constant because the parameter variations affect the net input of solar radiation at the top of the atmosphere (TOA). In contrast, there is a non-monotonic, peaked behaviour in the generation of APE and entropy production associated with kinetic energy dissipation, with the standard FAMOUS values for $$\epsilon$$ and c T occurring nearly at the maximising ones. The maximum states are shown to be states of vigorous baroclinic activity. The peak in the generation of APE appears to be related to a trade-off between the mean vertical stability and horizontal stratification. Experiments are repeated for a simplified setup in which the net solar input at TOA is fixed. Again a peak in the generation of APE is found in association with the maximum baroclinic activity, but no trade-off of the kind shown by simple climate models is found between meridional heat transport and the meridional temperature gradient. We conclude that the maximum entropy production conjecture does not hold within the climate system when the effects of the hydrological cycle and radiative feedbacks are taken into account, but our experiments provide some evidence in support of the conjecture of maximum APE production (or equivalently maximum dissipation of kinetic energy).

A step-response simple climate model to reconstruct and interpret AOGCM projections

[1] We propose a new simple climate modelling (SCM) framework for making fast climate projections and for interpreting global climate model (GCM) projections. This SCM is derived from CO2 step GCM experiments (a core integration in the fifth Climate Model Intercomparison Project CMIP5), and is similar to linear impulse-response theory. Its results are therefore traceable to GCM physics and closely related to a commonly-used method of analysing GCMs. Its formulation needs no tuning and permits clear validation. After discussing theoretical properties, we show that this SCM can work well, using the HadCM3 GCM for global temperature, precipitation, heat uptake and outgoing radiation under a range of forcing scenarios. The validation reveals interesting deviations from impulse-response linearity in surface temperature, radiative response and precipitation. These non-linearities emerge not just in climate feedback (as previously reported), but also in the rapid adjustment to forcing change. Some commonality is found between mechanisms by which temperature and precipitation depart from impulse-response linearity. Our results suggest that in support of the 4xCO2 experiments within CMIP5, additional 2xCO2 integrations from other GCMs could be very valuable. We also show how our framework can help understand time-dependent scenario projections, and suggest that this approach could be used as part of a process where GCM understanding and SCM formulation develop together.

SEEPLUS: A SIMPLE ONLINE CLIMATE MODEL

A web application for a simple climate model - SEEPLUS (a Simple climate model to Examine Emission Pathways Leading to Updated Scenarios) - has been developed. SEEPLUS consists of carbon-cycle and climate-change modules, through which it provides the information infrastructure required to perform climate-change experiments, even on a millennial-timescale. The main objective of this application is to share the latest scientific knowledge acquired from climate modeling studies among the different stakeholders involved in climate-change issues. Both the carbon-cycle and climate-change modules employ impulse response functions (IRFs) for their key processes, thereby enabling the model to integrate the outcome from an ensemble of complex climate models. The current IRF parameters and forcing manipulation are basically consistent with, or within an uncertainty range of, the understanding of certain key aspects such as the equilibrium climate sensitivity and ocean CO2 uptake data documented in representative literature. The carbon-cycle module enables inverse calculation to determine the emission pathway required in order to attain a given concentration pathway, thereby providing a flexible way to compare the module with more advanced modeling studies. The module also enables analytical evaluation of its equilibrium states, thereby facilitating the long-term planning of global warming mitigation.

Global Warming and Local Dimming: The Statistical Evidence

Two effects largely determine global warming: the well-known greenhouse effect and the less well-known solar radiation effect. An increase in concentrations of carbon dioxide and other greenhouse gases contributes to global warming: the greenhouse effect. In addition, small particles, called aerosols, reflect and absorb sunlight in the atmosphere. More pollution causes an increase in aerosols, so that less sunlight reaches the Earth (global dimming). Despite its name, global dimming is primarily a local (or regional) effect. Because of the dimming the Earth becomes cooler: the solar radiation effect. Global warming thus consists of two components: the (global) greenhouse effect and the (local) solar radiation effect, which work in opposite directions. Only the sum of the greenhouse effect and the solar radiation effect is observed, not the two effects separately. Our purpose is to identify the two effects. This is important, because the existence of the solar radiation effect obscures the magnitude of the greenhouse effect. We propose a simple climate model with a small number of parameters. We gather data from a large number of weather stations around the world for the period 1959–2002. We then estimate the parameters using dynamic panel data methods, and quantify the parameter uncertainty. Next, we decompose the estimated temperature change of 0.73 ◦C (averaged over the weather stations) into a greenhouse effect of 1.87 ◦C, a solar radiation effect of −1.09 ◦C, and a small remainder term. Finally, we subject our findings to extensive sensitivity analyses.

On long range dependence in global surface temperature series

Long Range Dependence (LRD) scaling behavior has been argued to characterize long-term surface temperature time series. LRD is typically measured by the so-called “Hurst” coefficient, “H”. Using synthetic temperature time series generated by a simple climate model with known physics, I demonstrate that the values of H obtained for observational temperature time series can be understood in terms of the linear response to past estimated natural and anthropogenic external radiative forcing combined with the effects of random white noise weather forcing. The precise value of H is seen to depend on the particular noise realization. The overall distribution obtained over an ensemble of noise realizations is seen to be a function of the relative amplitude of external forcing and internal stochastic variability and additionally in climate “proxy” records, the amount of non-climatic noise present. There is no obvious reason to appeal to more exotic physics for an explanation of the apparent scaling behavior in observed temperature data.