scholarly journals Climate sensitivity estimates – sensitivity to radiative forcing time series and observational data

2017 ◽  
Author(s):  
Ragnhild Bieltvedt Skeie ◽  
Terje Berntsen ◽  
Magne Aldrin ◽  
Marit Holden ◽  
Gunnar Myhre
Keyword(s):  
Time Series ◽  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Heat Content ◽  
Deep Ocean ◽  
Balance Model ◽  
Data Set ◽  
Surface Temperature Change ◽  
Near Surface ◽  
The Mean

Abstract. Inferred Effective Climate Sensitivity (ECSinf) is estimated using a method combining radiative forcing (RF) time series and several series of observed ocean heat content (OHC) and near-surface temperature change in a Bayesian-framework using a simple energy balance model and a stochastic model. The model is updated compared to our previous analysis by using recent forcing estimates from IPCC, including OHC data for the deep ocean, and extending the time series to 2014. The mean value of the estimated ECSinf is 2.0 °C, with a 90 % credible interval of 1.2–3.1 °C. The mean estimate has recently been shown to be consistent with the higher values for the equilibrium climate sensitivity estimated by climate models. We show a strong sensitivity of the estimated ECSinf to the choice of a priori RF time series, excluding pre-1950 data and the treatment of OHC data. Sensitivity analysis performed by merging the upper (0–700 m) and the deep ocean OHC or using only one OHC data set (instead of four in the main analysis), both give an enhancement the mean ECSinf by about 50 % from our best estimate.

scholarly journals Climate sensitivity estimates – sensitivity to radiative forcing time series and observational data

2018 ◽  
Vol 9 (2) ◽  
pp. 879-894 ◽  
Author(s):  
Ragnhild Bieltvedt Skeie ◽  
Terje Berntsen ◽  
Magne Aldrin ◽  
Marit Holden ◽  
Gunnar Myhre
Keyword(s):  
Time Series ◽  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Heat Content ◽  
Deep Ocean ◽  
Mean Value ◽  
Transient Climate Response ◽  
Surface Temperature Change ◽  
Near Surface ◽  
The Mean

Abstract. Inferred effective climate sensitivity (ECSinf) is estimated using a method combining radiative forcing (RF) time series and several series of observed ocean heat content (OHC) and near-surface temperature change in a Bayesian framework using a simple energy balance model and a stochastic model. The model is updated compared to our previous analysis by using recent forcing estimates from IPCC, including OHC data for the deep ocean, and extending the time series to 2014. In our main analysis, the mean value of the estimated ECSinf is 2.0 ∘C, with a median value of 1.9 ∘C and a 90 % credible interval (CI) of 1.2–3.1 ∘C. The mean estimate has recently been shown to be consistent with the higher values for the equilibrium climate sensitivity estimated by climate models. The transient climate response (TCR) is estimated to have a mean value of 1.4 ∘C (90 % CI 0.9–2.0 ∘C), and in our main analysis the posterior aerosol effective radiative forcing is similar to the range provided by the IPCC. We show a strong sensitivity of the estimated ECSinf to the choice of a priori RF time series, excluding pre-1950 data and the treatment of OHC data. Sensitivity analysis performed by merging the upper (0–700 m) and the deep-ocean OHC or using only one OHC dataset (instead of four in the main analysis) both give an enhancement of the mean ECSinf by about 50 % from our best estimate.

scholarly journals A lower and more constrained estimate of climate sensitivity using updated observations and detailed radiative forcing time series

2013 ◽  
Vol 4 (2) ◽  
pp. 785-852 ◽  
Author(s):  
R. B. Skeie ◽  
T. Berntsen ◽  
M. Aldrin ◽  
M. Holden ◽  
G. Myhre
Keyword(s):  
Time Series ◽  
Temperature Change ◽  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Heat Content ◽  
Ocean Heat Content ◽  
Balance Model ◽  
Surface Temperature Change ◽  
Near Surface ◽  
Posterior Mean

Abstract. The equilibrium climate sensitivity (ECS) is constrained based on observed near-surface temperature change, changes in ocean heat content (OHC) and detailed radiative forcing (RF) time series from pre-industrial times to 2010 for all main anthropogenic and natural forcing mechanism. The RF time series are linked to the observations of OHC and temperature change through an energy balance model and a stochastic model, using a Bayesian approach to estimate the ECS and other unknown parameters from the data. For the net anthropogenic RF the posterior mean in 2010 is 2.1 W m−2 with a 90% credible interval (C.I.) of 1.3 to 2.8 W m−2, excluding present day total aerosol effects (direct + indirect) stronger than −1.7 W m−2. The posterior mean of the ECS is 1.8 °C with 90% C.I. ranging from 0.9 to 3.2 °C which is tighter than most previously published estimates. We find that using 3 OHC data sets simultaneously substantially narrows the range in ECS, while using only one set and similar time periods can produce comparable results as previously published estimates including the heavy tail in the probability function. The use of additional 10 yr of data for global mean temperature change and ocean heat content data narrow the probability density function of the ECS. In addition when data only until year 2000 is used the estimated mean of ECS is 20% higher. Explicitly accounting for internal variability widens the 90% C.I. for the ECS by 60%, while the mean ECS only becomes slightly higher.

scholarly journals AirClim: an efficient tool for climate evaluation of aircraft technology

2008 ◽  
Vol 8 (16) ◽  
pp. 4621-4639 ◽  
Author(s):  
V. Grewe ◽  
A. Stenke
Keyword(s):  
Climate Change ◽  
Water Vapour ◽  
Radiative Forcing ◽  
Climate Impacts ◽  
Assessment Tools ◽  
Surface Temperature Change ◽  
Near Surface ◽  
Temperature Changes ◽  
Wide Range ◽  
Concentration Changes

Abstract. Climate change is a challenge to society and to cope with requires assessment tools which are suitable to evaluate new technology options with respect to their impact on global climate. Here we present AirClim, a model which comprises a linearisation of atmospheric processes from the emission to radiative forcing, resulting in an estimate in near surface temperature change, which is presumed to be a reasonable indicator for climate change. The model is designed to be applicable to aircraft technology, i.e. the climate agents CO2, H2O, CH4 and O3 (latter two resulting from NOx-emissions) and contrails are taken into account. AirClim combines a number of precalculated atmospheric data with aircraft emission data to obtain the temporal evolution of atmospheric concentration changes, radiative forcing and temperature changes. These precalculated data are derived from 25 steady-state simulations for the year 2050 with the climate-chemistry model E39/C, prescribing normalised emissions of nitrogen oxides and water vapour at various atmospheric regions. The results show that strongest climate impacts (year 2100) from ozone changes occur for emissions in the tropical upper troposphere (60 mW/m2; 80 mK for 1 TgN/year emitted) and from methane changes from emissions in the middle tropical troposphere (−2.7% change in methane lifetime; –30 mK per TgN/year). For short-lived species (e.g. ozone, water vapour, methane) individual perturbation lifetimes are derived depending on the region of emission. A comparison of this linearisation approach with results from a comprehensive climate-chemistry model shows reasonable agreement with respect to concentration changes, radiative forcing, and temperature changes. For example, the total impact of a supersonic fleet on radiative forcing (mainly water vapour) is reproduced within 10%. A wide range of application is demonstrated.

scholarly journals The Relationship between Land–Ocean Surface Temperature Contrast and Radiative Forcing

2011 ◽  
Vol 24 (13) ◽  
pp. 3239-3256 ◽  
Author(s):  
F. Hugo Lambert ◽  
Mark J. Webb ◽  
Manoj M. Joshi
Keyword(s):  
Surface Temperature ◽  
Heat Transport ◽  
Temperature Change ◽  
Land Surface ◽  
Radiative Forcing ◽  
Co2 Concentration ◽  
Ocean Surface ◽  
Heat Fluxes ◽  
Balance Model ◽  
Surface Temperature Change

Abstract Previous work has demonstrated that observed and modeled climates show a near-time-invariant ratio of mean land to mean ocean surface temperature change under transient and equilibrium global warming. This study confirms this in a range of atmospheric models coupled to perturbed sea surface temperatures (SSTs), slab (thermodynamics only) oceans, and a fully coupled ocean. Away from equilibrium, it is found that the atmospheric processes that maintain the ratio cause a land-to-ocean heat transport anomaly that can be approximated using a two-box energy balance model. When climate is forced by increasing atmospheric CO2 concentration, the heat transport anomaly moves heat from land to ocean, constraining the land to warm in step with the ocean surface, despite the small heat capacity of the land. The heat transport anomaly is strongly related to the top-of-atmosphere radiative flux imbalance, and hence it tends to a small value as equilibrium is approached. In contrast, when climate is forced by prescribing changes in SSTs, the heat transport anomaly replaces “missing” radiative forcing over land by moving heat from ocean to land, warming the land surface. The heat transport anomaly remains substantial in steady state. These results are consistent with earlier studies that found that both land and ocean surface temperature changes may be approximated as local responses to global mean radiative forcing. The modeled heat transport anomaly has large impacts on surface heat fluxes but small impacts on precipitation, circulation, and cloud radiative forcing compared with the impacts of surface temperature change. No substantial nonlinearities are found in these atmospheric variables when the effects of forcing and surface temperature change are added.

scholarly journals Vertical Redistribution of Oceanic Heat Content

2015 ◽  
Vol 28 (9) ◽  
pp. 3821-3833 ◽  
Author(s):  
Xinfeng Liang ◽  
Carl Wunsch ◽  
Patrick Heimbach ◽  
Gael Forget
Keyword(s):  
Heat Flux ◽  
Heat Exchange ◽  
Heat Transport ◽  
Heat Content ◽  
Deep Ocean ◽  
Temporal Variations ◽  
Near Surface ◽  
Carbon 14 ◽  
Vertical Heat ◽  
Vertical Heat Flux

Abstract Estimated values of recent oceanic heat uptake are on the order of a few tenths of a W m−2, and are a very small residual of air–sea exchanges, with annual average regional magnitudes of hundreds of W m−2. Using a dynamically consistent state estimate, the redistribution of heat within the ocean is calculated over a 20-yr period. The 20-yr mean vertical heat flux shows strong variations in both the lateral and vertical directions, consistent with the ocean being a dynamically active and spatially complex heat exchanger. Between mixing and advection, the two processes determining the vertical heat transport in the deep ocean, advection plays a more important role in setting the spatial patterns of vertical heat exchange and its temporal variations. The global integral of vertical heat flux shows an upward heat transport in the deep ocean, suggesting a cooling trend in the deep ocean. These results support an inference that the near-surface thermal properties of the ocean are a consequence, at least in part, of internal redistributions of heat, some of which must reflect water that has undergone long trajectories since last exposure to the atmosphere. The small residual heat exchange with the atmosphere today is unlikely to represent the interaction with an ocean that was in thermal equilibrium at the start of global warming. An analogy is drawn with carbon-14 “reservoir ages,” which range from over hundreds to a thousand years.

scholarly journals Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset

2021 ◽  
Vol 13 (24) ◽  
pp. 5074
Author(s):  
Feng Gao ◽  
Martha C. Anderson ◽  
David M. Johnson ◽  
Robert Seffrin ◽  
Brian Wardlow ◽  
...  
Keyword(s):  
Time Series ◽  
State Level ◽  
Growing Season ◽  
Absolute Difference ◽  
Corn Belt ◽  
Spatial And Temporal Variability ◽  
Field Scale ◽  
Near Surface ◽  
The Mean ◽  
Sentinel 2

Crop emergence is a critical stage for crop development modeling, crop condition monitoring, and biomass accumulation estimation. Green-up dates (or the start of the season) detected from remote sensing time series are related to, but generally lag, crop emergence dates. In this paper, we refine the within-season emergence (WISE) algorithm and extend application to five Corn Belt states (Iowa, Illinois, Indiana, Minnesota, and Nebraska) using routine harmonized Landsat and Sentinel-2 (HLS) data from 2018 to 2020. Green-up dates detected from the HLS time series were assessed using field observations and near-surface measurements from PhenoCams. Statistical descriptions of green-up dates for corn and soybeans were generated and compared to county-level planting dates and district- to state-level crop emergence dates reported by the National Agricultural Statistics Service (NASS). Results show that emergence dates for corn and soybean can be reliably detected within the season using the HLS time series acquired during the early growing season. Compared to observed crop emergence dates, green-up dates from HLS using WISE were ~3 days later at the field scale (30-m). The mean absolute difference (MAD) was ~7 days and the root mean square error (RMSE) was ~9 days. At the state level, the mean differences between median HLS green-up date and median crop emergence date were within 2 days for 2018–2020. At this scale, MAD was within 4 days, and RMSE was less than 5 days for both corn and soybeans. The R-squares were 0.73 and 0.87 for corn and soybean, respectively. The 2019 late emergence of crops in Corn Belt states (1–4 weeks to five-year average) was captured by HLS green-up date retrievals. This study demonstrates that routine within-season mapping of crop emergence/green-up at the field scale is practicable over large regions using operational satellite data. The green-up map derived from HLS during the growing season provides valuable information on spatial and temporal variability in crop emergence that can be used for crop monitoring and refining agricultural statistics used in broad-scale modeling efforts.

scholarly journals Observation-Based Estimates of Global and Basin Ocean Meridional Heat Transport Time Series

2019 ◽  
Vol 32 (14) ◽  
pp. 4567-4583 ◽  
Author(s):  
Kevin E. Trenberth ◽  
Yongxin Zhang ◽  
John T. Fasullo ◽  
Lijing Cheng
Keyword(s):  
Time Series ◽  
Interannual Variability ◽  
Annual Cycle ◽  
Decadal Variability ◽  
Southern Oscillation ◽  
Heat Content ◽  
Ocean Basin ◽  
Energy Budgets ◽  
Meridional Heat Transport ◽  
The Mean

Abstract Ocean meridional heat transports (MHTs) are deduced as a residual using energy budgets to produce latitude versus time series for the globe, Indo-Pacific, and Atlantic. The top-of-atmosphere (TOA) radiation is combined with the vertically integrated atmospheric energy divergence from atmospheric reanalyses to produce the net surface energy fluxes everywhere. The latter is then combined with estimates of the vertically integrated ocean heat content (OHC) tendency to produce estimates of the ocean heat divergence. Because seasonal sea ice and land runoff effects are not fully considered, the mean annual cycle is incomplete, but those effects are small for interannual variability. However, there is a mismatch between 12-month inferred surface flux and the corresponding OHC changes globally, requiring adjustments to account for the Earth’s global energy imbalance. Estimates are greatly improved by building in the constraint that MHT must go to zero at the northern and southern extents of the ocean basin at all times, enabling biases between the TOA and OHC data to be reconciled. Zonal mean global, Indo-Pacific, and Atlantic basin ocean MHTs are computed and presented as 12-month running means and for the mean annual cycle for 2000–16. For the Indo-Pacific, the tropical and subtropical MHTs feature a strong relationship with El Niño–Southern Oscillation (ENSO), and in the Atlantic, MHT interannual variability is significantly affected by and likely influences the North Atlantic Oscillation (NAO). However, Atlantic and Pacific changes are linked, suggesting that the northern annular mode (as opposed to NAO) is predominant. There is also evidence of decadal variability or trends.

A Time Series of Expendable Bathythermograph Sections Across the East Australian Current

1979 ◽  
Vol 30 (3) ◽  
pp. 303 ◽  
Author(s):  
FM Boland
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Deep Ocean ◽  
Seasonal Cycles ◽  
East Australian Current ◽  
Mean Sea Level ◽  
Lord Howe Island ◽  
Expendable Bathythermograph ◽  
Westward Movement ◽  
The Mean

Expendable bathythermograph sections of temperature were made eastward from Sydney to 156� E. at 2-week intervals over the period July 1969 to July 1975. The mean seasonal cycles of temperature at the surface and at 240 m depth are presented, as well as the time series of 240 m temperature. The results suggest a westward movement of disturbances and also imply a connection between measurements in the deep ocean and events on the continental shelf. The histogram of temperature at 240 m at 152� E. is very different from that at 154� E., the latter being distinctly bimodal. These histograms are compared with that of mean sea level at Lord Howe Island which is also bimodal.

scholarly journals Relating glacier mass balance to meteorological data by using a seasonal sensitivity characteristic

2000 ◽  
Vol 46 (152) ◽  
pp. 1-6 ◽  
Author(s):  
J. Oerlemans ◽  
B. K. Reichert
Keyword(s):  
Mass Balance ◽  
Climate Sensitivity ◽  
Significant Contribution ◽  
Meteorological Data ◽  
Balance Model ◽  
Glacier Mass Balance ◽  
Atmospheric Models ◽  
Temperature And Precipitation ◽  
The Mean ◽  
Sensitivity Characteristic

AbstractWe propose to quantify the climate sensitivity of the mean specific balance B of a glacier by a seasonal sensitivity characteristic (SSC). The SSC gives the dependence of B on monthly anomalies in temperature and precipitation. It is calculated from a mass-balance model. We show and discuss examples for Franz-Josef Glacier (New Zealand), Nigardsbreen (Norway), Hintereisferner (Austria), Peyto Glacier (Canadian Rockies), Abramov Glacier (Kirghizstan) and White Glacier (Canadian Arctic). With regard to the climate sensitivity of B, the SSCs clearly show that summer temperature is the most important factor for glaciers in a dry climate. For glaciers in a wetter climate, spring and fall temperatures also make a significant contribution to the overall sensitivity. The SSC is a 2 × 12 matrix. Multiplying it with monthly perturbations of temperature and precipitation for a particular year yields an estimate of the balance for that year. We show that, with this technique, mass-balance series can be (re)constructed from long meteorological records or from output of atmospheric models.

scholarly journals What could we learn about climate sensitivity from variability in the surface temperature record?

2020 ◽  
Author(s):  
James Douglas Annan ◽  
Julia Catherine Hargreaves ◽  
Thorsten Mauritsen ◽  
Bjorn Stevens
Keyword(s):  
Time Series ◽  
Climate Sensitivity ◽  
Internal Variability ◽  
Forced Response ◽  
Balance Model ◽  
Temperature Record ◽  
Full Time ◽  
Case Scenario ◽  
True Parameter ◽  
Equilibrium Climate Sensitivity

Abstract. We examine what can be learnt about climate sensitivity from variability in the surface air temperature record over the instrumental period, from around 1880 to the present. While many previous studies have used the trend in the time series to constrain equilibrium climate sensitivity, it has also been argued that temporal variability may also be a powerful constraint. We explore this question in the context of a simple widely used energy balance model of the climate system. We consider two recently-proposed summary measures of variability and also show how the full information content can be optimally used in this idealised scenario. We find that the constraint provided by variability is inherently skewed and its power is inversely related to the sensitivity itself, discriminating most strongly between low sensitivity values and weakening substantially for higher values. It is only when the sensitivity is very low that the variability can provide a tight constraint. Our investigations take the form of perfect model experiments, in which we make the optimistic assumption that the model is structurally perfect and all uncertainties (including the true parameter values and nature of internal variability noise) are correctly characterised. Therefore the results might be interpreted as a best case scenario for what we can learn from variability, rather than a realistic estimate of this. In these experiments, we find that for a moderate sensitivity of 2.5 °C, a 150 year time series of pure internal variability will typically support an estimate with a 5–95 % range of around 5 °C (e.g. 1.9–6.8 °C). Total variability including that due to the forced response, as observed in the detrended observational record, can provide a stronger constraint with an equivalent 5–95 % posterior range of around 4 °C (e.g. 1.7–5.6 °C) even when uncertainty in aerosol forcing is considered. Using a statistical summary of variability based on autocorrelation and the magnitude of residuals after detrending proves somewhat less powerful as a constraint than the full time series in both situations. Our results support the analysis of variability as a potentially useful tool in helping to constrain equilibrium climate sensitivity, but suggest caution in the interpretation of precise results.

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