Post tagged Climate Change

Separating physically distinct influences on Pacific sea-surface temperature variability

A key challenge in climate science is to distinguish temperature changes in response to external forcing (e.g., global warming in response to anthropogenic greenhouse gasses) from temperature changes due to atmosphere-ocean internal variability. Extended integrations of forced and unforced climate models are often used for this purpose. In Wills et al. (2018), we demonstrated a novel method called low-frequency component analysis (LFCA), which separates modes of internal variability from global warming based on differences in time scales and spatial patterns, without relying on climate models.

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Reducing Uncertainties in Climate Projections With Emergent Constraints. Part 2: Examples

In a previous post, I described the concept of emergent constraints, which allow us to narrow uncertainties in climate change projections through empirical relationships that relate a model’s climate response to observable metrics. The credibility of an emergent constraint relies upon the strength of the statistical relationship, a clear understanding of the mechanisms underlying the relationship, and the accuracy of observations. A number of emergent constraints have already been identified, with different weaknesses and strengths. This post aims to summarize some of them.

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Paris and the Future of Clouds

How low clouds respond to warming remains the greatest source of uncertainty in climate projections. Climate models projecting that much less sunlight will be reflected by low clouds when the climate warms indicate that CO2 concentrations can only reach 470 ppm before the 2℃ warming threshold of the Paris agreement is crossed—a CO2 concentration that will probably be reached in the 2030s. By contrast, models projecting a weak decrease or increase in low-cloud reflection indicate that CO2 concentrations may reach almost 600 ppm before the Paris threshold is crossed. In a new paper, we outline how new computational and observational tools enable us to reduce these vast uncertainties.

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The Role of the Surface Energy Balance in the Low-Cloud Response to Global Warming

Figure 1: Top-right: ISCCP low cloud cover (%) climatology for June-July-August, with the GPCI transect and the three locations used in our study (adapted from Teixeira et al. 2011). Bottom-left: schematic of cloud regimes in the tropical overturning circulations (Stevens 2005, adapted from Arakawa 1975).

Large-eddy simulation (LES) of clouds can help resolve one of the most important and challenging question in climate dynamics, namely, how subtropical low clouds respond to global warming. However, earlier LES studies have generally prescribed large-scale conditions (e.g., surface temperatures) in a way that does not guarantee energy balance. We have developed an energetically consistent framework for driving LES, in which the LES domain is coupled to a simple slab ocean. In this framework, the cloud responses to global warming can be very different than in the traditional frameworks that prescribe surface temperatures.

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Reducing Uncertainties in Climate Projections With Emergent Constraints. Part 1: Concept

Global-mean surface temperature anomaly relative to the end of the 19th century (1850-1900) for 29 models. Historical and RCP8.5 experiments are used. Colors scale with ECS values (from lowest in yellow to highest in red)

Steadily increasing carbon dioxide concentrations in the atmosphere are warming the Earth. Today (2006-2014) it is 0.8°C warmer than in the preindustrial period in the middle of the 19th century. Climate models try to project how this global warming will continue, but they differ in their response to increasing concentrations of greenhouse gases. Emergent constraints attempt to use information about the current climate to constrain the evolution of climate in the future.

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Constraints on Climate Sensitivity From Space-Based Measurements of Low-Cloud Reflection

Through their reflection of sunlight and absorption/re-emission of thermal radiation, clouds regulate Earth’s energy balance. But it remains uncertain, in particular, how the fraction of sunlight reflected by clouds will change as greenhouse gas concentrations rise. Projections differ widely among climate models, and differences in the solar reflection by low clouds over tropical oceans account for much of the spread in climate projections across current models. We investigate to what extent this uncertainty can be reduced through the use of observations from space.

A convenient yardstick to measure how sensitive the climate system is to increases in the concentration of greenhouse gases is the equilibrium climate sensitivity (ECS)—the surface warming eventually reached after a sustained doubling of carbon dioxide concentrations. ECS ranges from 2.1 to 4.7 K across current climate models (IPCC AR5).  More than half of the ECS variance across models can be traced to differences in the reflection of sunlight by tropical low clouds (TLCs) (Bony and Dufresne 2005; Vial et al. 2013). Neither the sign nor the strength of this TLC feedback are well constrained. Yet constraining the TLC feedback is essential for narrowing the wide range of ECS projected by current models.

A number of observational studies points to a weakening of solar reflection by TLCs under warming (Clement et al. 2009; Dessler 2010, 2013; Zhou et al. 2013), suggesting a positive TLC feedback. Other studies indicate that models with strongly positive low-cloud feedback are more consistent with observations than models with weakly positive or negative feedback (Qu et al. 2014, 2015b, Myers and Norris 2016). This is in line with other model–observation comparisons that also point to higher ECS (Fasullo and Trenberth 2012; Sherwood et al. 2014; Tian 2015). By contrast, studies focusing on Earth’s energy budget generally point to a lower ECS (Otto et al. 2013), albeit with large uncertainties that still allow a high ECS. In Brient and Schneider (2016), we show how space-based observations can be used to robustly constrain the TLC feedback and constrain ECS.

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Atmospheric Circulation Changes Control Patterns of Wetting and Drying with Global Warming

The hydrological cycle will change substantially in response to global warming. For the most part, wet regions will get wetter and dry regions will get drier as the amount of water the atmosphere can carry increases with warming. But regional patterns of precipitation minus evaporation are influenced by planetary-scale stationary waves, which are subject to substantial shifts and changes in strength as the planet warms. These stationary-wave changes lead to large regional changes in the hydrological cycle and modify the sensitivity of the hydrological cycle to global warming.

One of the most substantial climate changes in response to global warming is the increase in atmospheric water vapor content. Because of the increase in moisture content, existing wind patterns carry more moisture and strengthen the atmospheric branch of the hydrological cycle: storms bring more rainfall, wet regions get wetter, and dry regions get drier (Held and Soden 2006, O’Gorman and Schneider 2009).

Changes in the winds lead to further changes in the hydrological cycle with global warming. For example, there is an expansion of the subtropical dry zones associated with the poleward expansion of the Hadley circulation with global warming (Lu et al. 2009). Even bigger changes can result from shifts or changes in strength of tropical and subtropical convergence zones. These circulation changes lead to regional departures from the “wet gets wetter, dry gets drier” idea (Chou and Neelin 2004, Seager et al. 2010).

Wills et al. (2016) present an analysis of how circulation changes influence the global pattern of change in net precipitation (precipitation minus evaporation, P – E). The focus is on the east-west (or zonal) variations of P – E, and how they change with global warming. Here, we overview some of the findings from this paper.

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