scholarly journals Comment on “The Impact of Recent Forcing and Ocean Heat Uptake Data on Estimates of Climate Sensitivity”

2020 ◽  
Vol 33 (1) ◽  
pp. 391-396 ◽  
Author(s):  
Kevin Cowtan ◽  
Peter Jacobs
Keyword(s):  
Climate Model ◽  
Time Windows ◽  
Temperature Response ◽  
Internal Variability ◽  
Baseline Period ◽  
Historical Record ◽  
Ocean Heat Uptake ◽  
Transient Climate Response ◽  
Window Method ◽  
The Impact

AbstractIn 2018, Lewis and Curry presented a method for estimating the transient climate response (TCR) of the climate system from the temperature change between two time windows: an early baseline period in the nineteenth century and a modern period primarily in the twenty-first century. The results suggest a lower value of TCR than estimates from climate model simulations. Previous studies have identified uncertainty in the historical forcings, the impact of the time evolution of the forcing on temperature response, and observational issues as contributory factors to this disagreement. We investigate a further factor: uncertainty in the bias corrections applied to historical sea surface temperature data. This uncertainty can particularly affect the estimation of variables on decadal time scales and therefore affect the estimation of TCR using the window method as well as estimates of internal variability. We demonstrate that use of the whole historical record can mitigate the impacts of working with short time windows to some extent, particularly with respect to the early part of the record.

scholarly journals Reply to “Comment on ‘The Impact of Recent Forcing and Ocean Heat Uptake Data on Estimates of Climate Sensitivity’”

2020 ◽  
Vol 33 (1) ◽  
pp. 397-404 ◽  
Author(s):  
Nicholas Lewis ◽  
Judith Curry
Keyword(s):  
Time Windows ◽  
Internal Variability ◽  
Historical Record ◽  
Cmip5 Models ◽  
Temperature Record ◽  
Ocean Heat Uptake ◽  
Transient Climate Response ◽  
Total Uncertainty ◽  
The Impact ◽  
The Relationship

AbstractCowtan and Jacobs assert that the method used by Lewis and Curry in 2018 (LC18) to estimate the climate system’s transient climate response (TCR) from changes between two time windows is less robust—in particular against sea surface temperature bias correction uncertainty—than a method that uses the entire historical record. We demonstrate that TCR estimated using all data from the temperature record is closely in line with that estimated using the LC18 windows, as is the median TCR estimate using all pairs of individual years. We also show that the median TCR estimate from all pairs of decade-plus-length windows is closely in line with that estimated using the LC18 windows and that incorporating window selection uncertainty would make little difference to total uncertainty in TCR estimation. We find that, when differences in the evolution of forcing are accounted for, the relationship over time between warming in CMIP5 models and observations is consistent with the relationship between CMIP5 TCR and LC18’s TCR estimate but fluctuates as a result of multidecadal internal variability and volcanism. We also show that various other matters raised by Cowtan and Jacobs have negligible implications for TCR estimation in LC18.

What warms the warmest waters during El Niño?

2021 ◽  
Author(s):  
Allison Hogikyan ◽  
Stephan Fueglistaler ◽  
Laure Resplandy
Keyword(s):  
Surface Temperature ◽  
Climate Model ◽  
Deep Convection ◽  
Internal Variability ◽  
Historical Record ◽  
Forced Response ◽  
Ocean Dynamics ◽  
Ocean Heat Uptake ◽  
Eastern Equatorial Pacific ◽  
Heat Uptake

<p>During El Niño, the upwelling in the eastern equatorial Pacific (EEP) slows, leading to a warm sea surface temperature (SST) anomaly, and the tropical troposphere warms. Only SSTs in regions with atmospheric deep convection, typically the warmest SSTs, affect the temperature of the tropical free troposphere. The warming of the EEP, which is home to the coldest tropical SSTs and does not experience atmospheric convection, therefore appears insufficient to explain the observed warming of the troposphere. Here, we examine the physical processes that lead to the warming of the warmest SSTs using both a global atmosphere-ocean coupled climate model and the ECMWF reanalysis. We show that SSTs in convecting regions do not warm as a result of ocean dynamics (upwelling), but as a result of a net heat flux from the atmosphere to the ocean following a weakening of surface winds and decrease in evaporation. This increased ocean heat uptake in convecting regions opposes the decrease in ocean heat uptake in the rest of the tropics during El Nino. This process may be important for linking surface temperature to ocean heat uptake changes, and the contribution of internal variability in the form of ENSO and IPO to the forced response observed over the historical record.</p>

scholarly journals The Interpretation and Use of Biases in Decadal Climate Predictions

2014 ◽  
Vol 27 (8) ◽  
pp. 2931-2947 ◽  
Author(s):  
Ed Hawkins ◽  
Buwen Dong ◽  
Jon Robson ◽  
Rowan Sutton ◽  
Doug Smith
Keyword(s):  
Climate Model ◽  
Coupled Model ◽  
Internal Variability ◽  
Time Dependent ◽  
Start Time ◽  
Transient Climate Response ◽  
Global Mean Temperature ◽  
Decadal Climate ◽  
Highly Correlated ◽  
Hadley Centre

Abstract Decadal climate predictions exhibit large biases, which are often subtracted and forgotten. However, understanding the causes of bias is essential to guide efforts to improve prediction systems, and may offer additional benefits. Here the origins of biases in decadal predictions are investigated, including whether analysis of these biases might provide useful information. The focus is especially on the lead-time-dependent bias tendency. A “toy” model of a prediction system is initially developed and used to show that there are several distinct contributions to bias tendency. Contributions from sampling of internal variability and a start-time-dependent forcing bias can be estimated and removed to obtain a much improved estimate of the true bias tendency, which can provide information about errors in the underlying model and/or errors in the specification of forcings. It is argued that the true bias tendency, not the total bias tendency, should be used to adjust decadal forecasts. The methods developed are applied to decadal hindcasts of global mean temperature made using the Hadley Centre Coupled Model, version 3 (HadCM3), climate model, and it is found that this model exhibits a small positive bias tendency in the ensemble mean. When considering different model versions, it is shown that the true bias tendency is very highly correlated with both the transient climate response (TCR) and non–greenhouse gas forcing trends, and can therefore be used to obtain observationally constrained estimates of these relevant physical quantities.

Large ensemble assessment of how the global surface warming response to cumulative carbon differs for negative and positive carbon emissions  

2021 ◽  
Author(s):  
Negar Vakilifard ◽  
Katherine Turner ◽  
Ric Williams ◽  
Philip Holden ◽  
Neil Edwards ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Radiative Forcing ◽  
Climate Model ◽  
Climate Feedback ◽  
Climate Response ◽  
Ocean Heat Uptake ◽  
Transient Climate Response ◽  
Surface Warming ◽  
Radiative Processes ◽  
Negative Emission

<p>The controls of the effective transient climate response (TCRE), defined in terms of the dependence of surface warming since the pre-industrial to the cumulative carbon emission, is explained in terms of climate model experiments for a scenario including positive emissions and then negative emission over a period of 400 years. We employ a pre-calibrated ensemble of GENIE, grid-enabled integrated Earth system model, consisting of 86 members to determine the process of controlling TCRE in both CO<sub>2</sub> emissions and drawdown phases. Our results are based on the GENIE simulations with historical forcing from AD 850 including land use change, and the future forcing defined by CO<sub>2</sub> emissions and a non-CO<sub>2</sub> radiative forcing timeseries. We present the results for the point-source carbon capture and storage (CCS) scenario as a negative emission scenario, following the medium representative concentration pathway (RCP4.5), assuming that the rate of emission drawdown is 2 PgC/yr CO<sub>2</sub> for the duration of 100 years. The climate response differs between the periods of positive and negative carbon emissions with a greater ensemble spread during the negative carbon emissions. The controls of the spread in ensemble responses are explained in terms of a combination of thermal processes (involving ocean heat uptake and physical climate feedback), radiative processes (saturation in radiative forcing from CO<sub>2</sub> and non-CO<sub>2</sub> contributions) and carbon dependences (involving terrestrial and ocean carbon uptake).  </p>

scholarly journals The representation of solar cycle signals in stratospheric ozone. Part II: Analysis of global models

2017 ◽  
Author(s):  
Amanda C. Maycock ◽  
Katja Matthes ◽  
Susann Tegtmeier ◽  
Hauke Schmidt ◽  
Rémi Thiéblemont ◽  
...  
Keyword(s):  
Solar Cycle ◽  
Solar Irradiance ◽  
Climate Models ◽  
Climate Model ◽  
Stratospheric Ozone ◽  
Temperature Response ◽  
Solar Variability ◽  
High Latitudes ◽  
Stratospheric Temperature ◽  
The Impact

Abstract. The impact of changes in incoming solar irradiance on stratospheric ozone abundances should be included in climate model simulations to fully capture the atmospheric response to solar variability. This study presents the first systematic comparison of the solar-ozone response (SOR) during the 11 year solar cycle amongst different chemistry-climate models (CCMs) and ozone databases specified in climate models that do not include chemistry. We analyse the SOR in eight CCMs from the WCRP/SPARC Chemistry-Climate Model Initiative (CCMI-1) and compare these with three ozone databases: the Bodeker Scientific database, the SPARC/AC&C database for CMIP5, and the SPARC/CCMI database for CMIP6. The results reveal substantial differences in the representation of the SOR between the CMIP5 and CMIP6 ozone databases. The peak amplitude of theSOR in the upper stratosphere (1–5 hPa) decreases from 5 % to 2 % between the CMIP5 and CMIP6 databases. This difference is because the CMIP5 database was constructed from a regression model fit to satellite observations, whereas the CMIP6 database is constructed from CCM simulations, which use a spectral solar irradiance (SSI) dataset with relatively weak UV forcing. The SOR in the CMIP6 ozone database is therefore implicitly more similar to the SOR in the CCMI-1 models than to the CMIP5 ozone database, which shows a greater resemblance in amplitude and structure to the SOR in the Bodeker database. The latitudinal structure of the annual mean SOR in the CMIP6 ozone database and CCMI-1 models is considerably smoother than in the CMIP5 database, which shows strong gradients in the SOR across the midlatitudes owing to the paucity of observations at high latitudes. The SORs in the CMIP6 ozone database and in the CCMI-1 models show a strong seasonal dependence, including large meridional gradients at mid to high latitudes during winter; such seasonal variations in the SOR are not included in the CMIP5 ozone database. Sensitivity experiments with a global atmospheric model without chemistry (ECHAM6.3) are performed to assess the impact of changes in the representation of the SOR and SSI forcing between CMIP5 and CMIP6. The experiments show that the smaller amplitude of the SOR in the CMIP6 ozone database compared to CMIP5 causes a decrease in the modelled tropical stratospheric temperature response over the solar cycle of up to 0.6 K, or around 50 % of the total amplitude. The changes in the SOR explain most of the difference in the amplitude of the tropical stratospheric temperature response in the case with combined changes in SOR and SSI between CMIP5 and CMIP6. The results emphasise the importance of adequately representing the SOR in climate models to capture the impact of solar variability on the atmosphere. Since a number of limitations in the representation of the SOR in the CMIP5 ozone database have been identified, CMIP6 models without chemistry are encouraged to use the CMIP6 ozone database to capture the climate impacts of solar variability.

scholarly journals Bayesian estimation of Earth's climate sensitivity and transient climate response from observational warming and heat content datasets

2021 ◽  
Vol 12 (2) ◽  
pp. 709-723
Author(s):  
Philip Goodwin ◽  
B. B. Cael
Keyword(s):  
Climate Sensitivity ◽  
Future Climate ◽  
Climate Feedback ◽  
Future Climate Change ◽  
Climate Projections ◽  
Climate Response ◽  
Ocean Heat Uptake ◽  
Transient Climate Response ◽  
Posterior Probability Distribution ◽  
The Impact

Abstract. Future climate change projections, impacts, and mitigation targets are directly affected by how sensitive Earth's global mean surface temperature is to anthropogenic forcing, expressed via the climate sensitivity (S) and transient climate response (TCR). However, the S and TCR are poorly constrained, in part because historic observations and future climate projections consider the climate system under different response timescales with potentially different climate feedback strengths. Here, we evaluate S and TCR by using historic observations of surface warming, available since the mid-19th century, and ocean heat uptake, available since the mid-20th century, to constrain a model with independent climate feedback components acting over multiple response timescales. Adopting a Bayesian approach, our prior uses a constrained distribution for the instantaneous Planck feedback combined with wide-ranging uniform distributions of the strengths of the fast feedbacks (acting over several days) and multi-decadal feedbacks. We extract posterior distributions by applying likelihood functions derived from different combinations of observational datasets. The resulting TCR distributions when using two preferred combinations of historic datasets both find a TCR of 1.5 (1.3 to 1.8 at 5–95 % range) ∘C. We find the posterior probability distribution for S for our preferred dataset combination evolves from S of 2.0 (1.6 to 2.5) ∘C on a 20-year response timescale to S of 2.3 (1.4 to 6.4) ∘C on a 140-year response timescale, due to the impact of multi-decadal feedbacks. Our results demonstrate how multi-decadal feedbacks allow a significantly higher upper bound on S than historic observations are otherwise consistent with.

scholarly journals Exploring the uncertainties in the aviation soot–cirrus effect

2021 ◽  
Vol 21 (23) ◽  
pp. 17267-17289
Author(s):  
Mattia Righi ◽  
Johannes Hendricks ◽  
Christof Gerhard Beer
Keyword(s):  
Radiative Forcing ◽  
Climate Model ◽  
Internal Variability ◽  
Reanalysis Data ◽  
Ice Nucleation ◽  
Cirrus Clouds ◽  
Model Studies ◽  
Model Dynamics ◽  
Analogous Model ◽  
The Impact

Abstract. A global aerosol–climate model, including a two-moment cloud microphysical scheme and a parametrization for aerosol-induced ice formation in cirrus clouds, is applied in order to quantify the impact of aviation soot on natural cirrus clouds. Several sensitivity experiments are performed to assess the uncertainties in this effect related to (i) the assumptions on the ice nucleation abilities of aviation soot, (ii) the representation of vertical updrafts in the model, and (iii) the use of reanalysis data to relax the model dynamics (the so-called nudging technique). Based on the results of the model simulations, a radiative forcing from the aviation soot–cirrus effect in the range of −35 to 13 mW m−2 is quantified, depending on the assumed critical saturation ratio for ice nucleation and active fraction of aviation soot but with a confidence level below 95 % in several cases. Simple idealized experiments with prescribed vertical velocities further show that the uncertainties on this aspect of the model dynamics are critical for the investigated effect and could potentially add a factor of about 2 of further uncertainty to the model estimates of the resulting radiative forcing. The use of the nudging technique to relax model dynamics is proved essential in order to identify a statistically significant signal from the model internal variability, while simulations performed in free-running mode and with prescribed sea-surface temperatures and sea-ice concentrations are shown to be unable to provide robust estimates of the investigated effect. A comparison with analogous model studies on the aviation soot–cirrus effect show a very large model diversity, with a conspicuous lack of consensus across the various estimates, which points to the need for more in-depth analyses on the roots of such discrepancies.

scholarly journals Chemistry–climate interactions of aerosol nitrate from lightning

2017 ◽  
Vol 17 (2) ◽  
pp. 1125-1142 ◽  
Author(s):  
Holger Tost
Keyword(s):  
Climate Model ◽  
Statistical Significance ◽  
Aerosol Particles ◽  
Internal Variability ◽  
Aerosol Size Distribution ◽  
Upper Troposphere ◽  
Uncertainty Range ◽  
Mixing Ratios ◽  
Microphysical Properties ◽  
The Impact

Abstract. Lightning represents one of the dominant emission sources for NOx in the troposphere. The direct release of oxidised nitrogen in the upper troposphere does not only affect ozone formation, but also chemical and microphysical properties of aerosol particles in this region. This study investigates the direct impact of LNOx emissions on upper-tropospheric nitrate using a global chemistry climate model. The simulation results show a substantial influence of the lightning emissions on the mixing ratios of nitrate aerosol in the upper troposphere of more than 50 %. In addition to the impact on nitrate, lightning substantially affects the oxidising capacity of the atmosphere with substantial implications for gas-phase sulfate formation and new particle formation in the upper troposphere. In conjunction with the condensation of nitrates, substantial differences in the aerosol size distribution occur in the upper troposphere as a consequence of lightning. This has implications for the extinction properties of the aerosol particles and for the cloud optical properties. While the extinction is generally slightly enhanced due to the LNOx emissions, the response of the clouds is ambiguous due to compensating effects in both liquid and ice clouds. Resulting shortwave flux perturbations are of   ∼ −100 mW m−2 as determined from several sensitivity scenarios, but an uncertainty range of almost 50 % has to be defined due to the large internal variability of the system and the uncertainties in the multitude of involved processes. Despite the clear statistical significance of the influence of lightning on the nitrate concentrations, the robustness of the findings gradually decreases towards the determination of the radiative flux perturbations.

Impact of climate change on water requirements and growth of potato in different climatic zones of Montenegro

2018 ◽  
Vol 9 (4) ◽  
pp. 657-671 ◽  
Author(s):  
Mirko Knežević ◽  
Ljubomir Zivotić ◽  
Nataša Čereković ◽  
Ana Topalović ◽  
Nikola Koković ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Regional Climate ◽  
Baseline Period ◽  
Yield Reduction ◽  
Adaptation Measures ◽  
Impact Of Climate Change ◽  
Temperature And Precipitation ◽  
Viable Solution ◽  
The Impact

Abstract The impact of climate change on potato cultivation in Montenegro was assessed. Three scenarios (A1B, A1Bs and A2) for 2001–2030, 2071–2100 and 2071–2100, respectively, were generated by a regional climate model and compared with the baseline period 1961–1990. The results indicated an increase of temperature during the summer season from 1.3 to 4.8 °C in the mountain region and from 1 to 3.4 °C in the coastal zone. The precipitation decreased between 5 and 50% depending on the scenario, region and season. The changes in temperature and precipitation influenced phenology, yield and water needs. The impact was more pronounced in the coastal areas than in the mountain regions. The growing season was shortened 13.6, 22.9 and 29.7 days for A1B, A1Bs and A2, respectively. The increase of irrigation requirement was 4.0, 19.5 and 7.3 mm for A1B, A1Bs and A2, respectively. For the baseline conditions, yield reduction under rainfed cultivation was lower than 30%. For A1B, A1Bs and A2 scenarios, yield reductions were 31.0 ± 8.2, 36.3 ± 11.6 and 34.1 ± 10.9%, respectively. Possible adaptation measures include shifting of production to the mountain (colder) areas and irrigation application. Rainfed cultivation remains a viable solution when the anticipation of sowing is adopted.

scholarly journals On the Choice of Ensemble Mean for Estimating the Forced Signal in the Presence of Internal Variability

2018 ◽  
Vol 31 (14) ◽  
pp. 5681-5693 ◽  
Author(s):  
Leela M. Frankcombe ◽  
Matthew H. England ◽  
Jules B. Kajtar ◽  
Michael E. Mann ◽  
Byron A. Steinman
Keyword(s):  
Regional Differences ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
Single Model ◽  
Sea Level Pressure ◽  
Model Simulations ◽  
Ensemble Mean ◽  
Climate Model Simulations ◽  
The Impact

Abstract In this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variability. We find that an ensemble mean of runs from a single climate model [a single model ensemble mean (SMEM)] provides a good estimate of the true forced signal even for models with very few ensemble members. In cases where only a single member is available for a given model, however, the SMEM from other models is in general out-performed by the scaled ensemble mean from all available climate model simulations [the multimodel ensemble mean (MMEM)]. The scaled MMEM may therefore be used as an estimate of the forced signal for observations. The MMEM method, however, leads to increasing errors further into the future, as the different rates of warming in the models causes their trajectories to diverge. We therefore apply the SMEM method to those models with a sufficient number of ensemble members to estimate the change in the amplitude of internal variability under a future forcing scenario. In line with previous results, we find that on average the surface air temperature variability decreases at higher latitudes, particularly over the ocean along the sea ice margins, while variability in precipitation increases on average, particularly at high latitudes. Variability in sea level pressure decreases on average in the Southern Hemisphere, while in the Northern Hemisphere there are regional differences.

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