Understanding the surface temperature response and its uncertainty to CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;, CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt;, black carbon, and sulfate

Nordling, Kalle; Korhonen, Hannele; Räisänen, Jouni; Partanen, Antti‐Ilari; Samset, B. H.; Merikanto, Joonas

doi:10.5194/acp-21-14941-2021

Cited by 3 publications

(3 citation statements)

References 54 publications

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“…2a). Our results are consistent with Nordling et al (2021) who show the higher sensitivity to non-CO2 forcing compared to CO2 forcing (Figure S4).…”

Section: Climate Impactssupporting

confidence: 93%

“…This physiological warming occurs because plants close their stomata under elevated CO2, reducing transpiration and decreasing latent heat loss which causes a local surface warming. Nordling et al (2021) further demonstrated that change in top of the atmosphere (TOA) long-wave clear-sky emissivity is the key driver for the temperature response differences between GHGs forcings. Using Earth System model simulations, Tokarska et al (2018) showed that non-CO2 forcing reduces the remaining carbon budget due to its direct radiative effects on surface temperature, causing additional warming.…”

Section: Introductionmentioning

confidence: 99%

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Carbon cycle and climate feedback under CO₂ and non-CO₂ overshoot pathways

Melnikova,

Ciais,

Tanaka

et al. 2024

Preprint

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Abstract. Reducing emissions of non-CO2 greenhouse gases complements CO2 mitigation in limiting global warming. However, estimating carbon-climate feedback for these gases remains fraught with uncertainties, especially under overshoot scenarios. This study investigates how CO2 and non-CO2 gases with nearly equal effective radiative forcing magnitudes impact the climate and carbon cycle using the Earth System Model IPSL-CM6A-LR. We first present a method to recalibrate methane and nitrous oxide concentrations to align with published radiative forcings, ensuring accurate model performance. Next, we carry out a series of idealised ramp-up and ramp-down concentration-driven experiments and show that while the impacts of increasing and decreasing CO2 and non-CO2 gases on the surface climate are nearly equivalent (when their radiative forcing magnitudes are set to be the same), regional differences emerge. We further explore the carbon cycle feedback and demonstrate that they differ under CO2 and non-CO2 forcing. CO2 forcing primarily affects temperature-driven feedback, whereas non-CO2 gases influence both temperature and carbon-concentration feedback. We introduce a novel framework to separate the carbon-climate feedback into temperature and cross terms, revealing that these components are comparable in magnitude for the global ocean. This highlights the importance of considering both components in Earth System modelling and climate change mitigation strategies.

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“…2a). Our results are consistent with Nordling et al (2021) who show the higher sensitivity to non-CO2 forcing compared to CO2 forcing (Figure S4).…”

Section: Climate Impactssupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Carbon cycle and climate feedback under CO₂ and non-CO₂ overshoot pathways

Melnikova,

Ciais,

Tanaka

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…A shift from high α when black carbon dominates (around 1925) to low α as sulphates become more important (1950 onwards) would be consistent with the finding by Ceppi and Gregory (2019) of larger α (lower sensitivity) from black carbon than sulphate forcing. This is an uncertain explanation, however, as other works found opposite results (Nordling et al., 2021; Richardson et al., 2019). Furthermore, feedbacks remain closer to the value from regression across the full period after forcing becomes more significant ( i .…”

Section: Time‐evolution Of Radiative Feedbackmentioning

confidence: 81%

Time‐Evolving Radiative Feedbacks in the Historical Period

Salvi,

Gregory,

Ceppi

2023

JGR Atmospheres

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We investigate the time‐dependence of radiative feedback in the historical period (since the late 19th century), by analysing experiments using coupled atmosphere–ocean climate models with historical greenhouse gas, anthropogenic aerosol, and natural forcings, each separately. We find that radiative feedback depends on forcing agent, primarily through the effect of cloud on shortwave radiation, because the various forcings cause different changes in global‐mean tropospheric stability per degree of global‐mean temperature change. The large time‐variation of historical feedback driven by observed sea surface temperature change alone, with no forcing agents, is also consistent with tropospheric stability change, and differs from the similarly large and significant historical time‐variation of feedback that is simulated in response to all forcing agents together. We show that the latter results from the varying relative sizes of individual forcings. We highlight that volcanic forcing is especially important for understanding the time‐variation, because it stimulates particularly strong feedbacks that tend to reduce effective climate sensitivity. We relate this to stability changes due to enhanced surface temperature response in the Indo‐Pacific warm pool.

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On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration

Wi,

Steinschneider

2024

Hydrol. Earth Syst. Sci.

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Abstract. Deep learning (DL) rainfall–runoff models outperform conceptual, process-based models in a range of applications. However, it remains unclear whether DL models can produce physically plausible projections of streamflow under climate change. We investigate this question through a sensitivity analysis of modeled responses to increases in temperature and potential evapotranspiration (PET), with other meteorological variables left unchanged. Previous research has shown that temperature-based PET methods overestimate evaporative water loss under warming compared with energy budget-based PET methods. We therefore assume that reliable streamflow responses to warming should exhibit less evaporative water loss when forced with smaller, energy-budget-based PET compared with temperature-based PET. We conduct this assessment using three conceptual, process-based rainfall–runoff models and three DL models, trained and tested across 212 watersheds in the Great Lakes basin. The DL models include a Long Short-Term Memory network (LSTM), a mass-conserving LSTM (MC-LSTM), and a novel variant of the MC-LSTM that also respects the relationship between PET and evaporative water loss (MC-LSTM-PET). After validating models against historical streamflow and actual evapotranspiration, we force all models with scenarios of warming, historical precipitation, and both temperature-based (Hamon) and energy-budget-based (Priestley–Taylor) PET, and compare their responses in long-term mean daily flow, low flows, high flows, and seasonal streamflow timing. We also explore similar responses using a national LSTM fit to 531 watersheds across the United States to assess how the inclusion of a larger and more diverse set of basins influences signals of hydrological response under warming. The main results of this study are as follows: The three Great Lakes DL models substantially outperform all process-based models in streamflow estimation. The MC-LSTM-PET also matches the best process-based models and outperforms the MC-LSTM in estimating actual evapotranspiration. All process-based models show a downward shift in long-term mean daily flows under warming, but median shifts are considerably larger under temperature-based PET (−17 % to −25 %) than energy-budget-based PET (−6 % to −9 %). The MC-LSTM-PET model exhibits similar differences in water loss across the different PET forcings. Conversely, the LSTM exhibits unrealistically large water losses under warming using Priestley–Taylor PET (−20 %), while the MC-LSTM is relatively insensitive to the PET method. DL models exhibit smaller changes in high flows and seasonal timing of flows as compared with the process-based models, while DL estimates of low flows are within the range estimated by the process-based models. Like the Great Lakes LSTM, the national LSTM also shows unrealistically large water losses under warming (−25 %), but it is more stable when many inputs are changed under warming and better aligns with process-based model responses for seasonal timing of flows. Ultimately, the results of this sensitivity analysis suggest that physical considerations regarding model architecture and input variables may be necessary to promote the physical realism of deep-learning-based hydrological projections under climate change.

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Understanding the surface temperature response and its uncertainty to CO<sub>2</sub>, CH<sub>4</sub>, black carbon, and sulfate

Cited by 3 publications

References 54 publications

Carbon cycle and climate feedback under CO₂ and non-CO₂ overshoot pathways

Carbon cycle and climate feedback under CO₂ and non-CO₂ overshoot pathways

Time‐Evolving Radiative Feedbacks in the Historical Period

On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration

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Understanding the surface temperature response and its uncertainty to CO&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt;, black carbon, and sulfate

Cited by 3 publications

References 54 publications

Carbon cycle and climate feedback under CO2 and non-CO2 overshoot pathways

Carbon cycle and climate feedback under CO2 and non-CO2 overshoot pathways

Time‐Evolving Radiative Feedbacks in the Historical Period

On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration

Contact Info

Product

Resources

About

Understanding the surface temperature response and its uncertainty to CO<sub>2</sub>, CH<sub>4</sub>, black carbon, and sulfate

Carbon cycle and climate feedback under CO₂ and non-CO₂ overshoot pathways

Carbon cycle and climate feedback under CO₂ and non-CO₂ overshoot pathways