I just read this fascinating story on RealClimate.org about some authors who decided to publish a paper about reproducing a group of contrarian papers that is routinely cited by people and organizations actively trying to discredit climate science, such as The Heartland Institute. The story of the series of rejections by various journals can be found here. Whatever stance you have about climate change, I hope we can all agree that replicating previous studies to learn from mistakes is a valuable exercise.

Benestad, R. E., D. Nuccitelli, S. Lewandowsky, K. Hayhoe, H.O. Hygen, R. van Dorland, and J. Cook, 2015: Learning from mistakes in climate research, Theoretical and Applied Climatology.

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Understanding how the global-mean precipitation rate will change in response to a climate forcing is a useful thing to know. We have strong evidence that the hydrologic cycle will become more intense in response to CO₂, but quantifying what drives this change is a bit more complicated, and can be understood from a few different perspectives. This paper takes a unique approach that really helped my understanding of the problem.

Pendergrass, A. G. and D. L. Hartmann, 2014: The Atmospheric Energy Constraint on Global-Mean Precipitation Change. J. Climate, 27, 757–768.

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This is one of the early seminal atmospheric modeling papers that dealt with the effects of anthropogenic CO₂. Of course, we knew about the basics of how increased CO₂ would affect the atmosphere ever since John Tyndall’s work in 1861, along with many others. Manabe and Wetherald were some of the first to explore this problem with an atmospheric model. This was made possible by the availability of computing power, which was paltry compared to the computers of today. Maybe even paltry when compared to a modern smartphone!

Manabe, S. and R. T. Wetherald, 1975: The Effects of Doubling the CO2 Concentration on the climate of a General Circulation Model. J. Atmos. Sci., 32, 3–15.

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Diaz, M., and A. Aiyyer, 2013: Energy Dispersion in African Easterly Waves. J. Atmos. Sci., 70, 130–

This study sets out to understand why African easterly waves (AEW) have a westward phase speed, but an eastward group velocity. Previous studies of AEW energetics haven’t really considered this aspect of AEWs, but this study presents a convincing case that techniques used to understand midlatitude baroclinic waves can be very useful to understand AEW dynamics. Continue reading →

I see this paper cited a lot in the climate literature, because it was one of the early papers that established the idea that the response of global mean hydrologic cycle is constrained by the net radiation at the surface.

Boer, G. (1993). Climate change and the regulation of the surface moisture and energy budgets. Climate Dynamics, 8, 225–239.  Continue reading →

I realized that I need to brush up on the classic literature about global warming. This paper seemed like a good place to start.

V. Ramanathan, 1981: The Role of Ocean-Atmosphere Interactions in the CO2 Climate Problem. J. Atmos. Sci., 38, 918–930. Continue reading →

Lappen, C.-L., and C. Schumacher (2014), The role of tilted heating in the evolution of the MJO, J. Geophys. Res. Atmos., 119, 2966–2989.

The Madden-Julian Oscillation (MJO) has many curious features that currently have evaded a fundamental understanding. One such feature is the “westward tilt with height” that is often seen in analysis such as this lagged humidity composites from Kiladis et al. (2005), Continue reading →

Chikira, M., 2014: Eastward-Propagating Intraseasonal Oscillation Represented by Chikira Sugiyama Cumulus Parameterization. Part II: Understanding Moisture Variation under Weak Temperature Gradient Balance. J. Atmos. Sci., 71, 615–639.

A recent paper by Chikira (2014) has changed the way I think about moisture mode theory and the Madden Julian Oscillation (MJO). If you’re not familiar with the MJO or moisture mode theory, then this post is gonna be over your head (sorry). But if you’ve been following the decades long search to explain what the MJO is and how it works, then I encourage you to read this paper. Continue reading →

Judd, K., C. A. Reynolds, T. E. Rosmond, and L. A. Smith, 2008: The geometry of model error. J. Atmos. Sci., 65, 1749–1772.

Recently I’ve been working on “hindcasting” weather events with a climate model. A hindcast is just a forecast, but you do it after the event has happened. Many climate models have issues in how they represent the relationship between convection and environmental moisture. It is difficult to compare the variability in a climate simulation and observations, because the long-term averages tend to be slightly different in each case in such a way that affects the variability itself. So, the idea behind these hindcasts is to compare a climate model to observations in the short period after model initialization, when they are much closer to each other.

A common problem in this type of work is that the models quickly “drift” away from the initial state. This drift is sometimes referred to as “model error”. Up till now, I’ve been thinking about model error as a problem with the model that needs to be corrected. However, this new paper by Judd et al. (2008) has changed my thinking on this issue. Continue reading →