Dr. Gavin A. Schmidt

Research Interests

My main research interest lies in understanding the variability of the climate, both its internal variability and the response to external forcing. In particular, how changes related to varying forcings relate to variations due to intrinsic (unforced) climate variability such as oscillations in the ocean's deep thermohaline circulation that affect ocean heat transports or atmospheric modes of variability like the North Atlantic Oscillation. I mainly use large-scale Earth System Models for the atmosphere, ocean, ice and land to investigate these questions. The most recent update to the GISS ModelE is described in Schmidt et al. (2014a).

I am particularly interested in ways in which model skill can be evaluated over the instrumental period and in paleo-climate records, with a focus on periods that might provide key constraints on the system (Schmidt, 2010). I recently coordinated a white paper on how these measures of skill in representing past climate changes can be directly used to inform future projections (Schmidt et al,., 2014b).

The evidence of long term paleo-climate variability exists primarily in the form of proxy data recorded in deep sea cores, ice cores, tree rings and other proxies such as the the skeletal remains of corals or in speleothems (cave deposits). The main difficulty is that the proxy data are records of multiple processes and hence, it is difficult to unambiguously ascribe a climatic cause to any particular recorded event, hence incorporating 'forward models' of the proxies themselves is a key step in being able to assess the proxy 'fingerprints' of change.

Specific Interests

Earth System Model Development

I am the Principal Investigator for the GISS ModelE Earth System Model. This model was used for the GISS modelling contribution to the CMIP3 and CMIP5 databases, which have been widely used by the IPCC 4th and 5th Assessment Reports (AR4/AR5).

The source code, documentation and external datasets for running the model (in a number of different configurations) are available at the ModelE website. As model runs and CMIP simulations are completed, the output will be made available as well. Feel free to do your own runs! (The basic version of the model will run on almost any platform PC (Linux), Mac or Unix - see the system requirements in the documentation for details).

The evaluation for the atmospheric component for CMIP3 was published in (Schmidt et al (2006)), while a rather technical discussion on how ocean-ice-atmospheric boundary conditions should be handled in such models appeared in Ocean Modelling Schmidt et al. (2004). Papers describing the coupled model climatologies, results and the sensitivity to many different forcings are also available: Hansen et al (2005a). Results from simulations over the 20th Century are in Hansen et al (2007a) and future projections are described in Hansen et al (2007b).

Descriptions of the upgrades to the code for CMIP5 are described in Schmidt et al. (2014) and preliminary results for the chemistry simulations, historical (20th Century) runs, and future projections are described in Shindell et al. (2013), Miller et al. (2014), and Nazarenko et al. (subm), respectively.

Water isotopes in the climate system

The principle proxy used for inferring information about past climate conditions is the oxygen-18 ratio measured in carbonate found in deep sea sediments (and corals) or in ice cores and speleothems (cave deposits). This ratio is a function of multiple effects: the background isotopic ratio in the seawater and the local temperature as the carbonate is secreted in the ocean, temperatures and source waters in the atmosphere. Understanding the variability of the seawater oxygen-18 signal and it's relationship to changes in climate is thus essential to interpreting the carbonate record through time. (There are two related Science briefs: Cold Climates, Warm Climates: How Can We Tell Past Temperatures? and Tracing the Water Cycle, Isotopically which explain this in a simple way). The CMIP3 version of the atmospheric model including these tracers is described in Schmidt et al (2005), and the application of this model to simulating isotopic fingerprints of past climate change is described in Schmidt et al (2007). More specific applications have been published for the 8.2kyr event (LeGrande et al, 2006; 2008) and for the mid-Holocene (Oppo et al, 2007). We have also discussed the isotopic fingerprints of Heinrich effects Lewis et al. (2011) and the use of deuterium excess as a water vapour tracer Lewis et al. (2013).

One application of these results concerns the relationship of oxygen-18 to salinity in the oceans. This relationship is mostly linear though modelling studies can indicate where this assumption may break down (Legrande and Schmidt, 2011). This has important implications for paleosalinity calculations.

In order to validate the ocean simulations, I and my collaborators have amassed a collection of well over 20,000 data points of oceanic measurements. The Global Seawater Oxygen-18 database is available on-line in a searchable form as well as a gridded 3-D data set (LeGrande and Schmidt, 2007) and accessible by other interested observers. Any additional contributions to this database are most welcome.

Going further in comparing models and the proxy data is to forward model the signal that would be recorded in the sediments or corals given a modelled climatic event. When the oxygen-18 ratio in the seawater is combined with simple ecologic models of foraminifera or coral growth it can provide a mapping of a particular modelled climatic event (meltwater pulses, changes in atmospheric forcing etc.) to the isotopic signal that would be recorded in carbonate sediments. In addition, using a range of plausible assumptions in the biological models (i.e. seasonal succession, depth variability), the extent to which our uncertainty about the ecology limits the accuracy of the derived climate records can be investigated. This method is a promising approach to tackling the inverse problem: what do observed changes in carbonate proxy data imply about past climate changes?

A number of papers have addressed this issue, Schmidt (1999), Schmidt and Mulitza (2002). (Note that colour versions of the black and white figures are available here as well), and this technique was applied to the 8.2 kyr event (see Science Brief) in LeGrande et al (2006).

Modelling recent climate change

This topic is a main focus of work at GISS. We have spent a lot of time trying to understand and evaluate potential impacts of different climate drivers on modes of climate variability. In particular, we have looked carefully at whether changes in the Annular modes around both poles can be attributed to different forcings. We have identified some key sensitivities (to model resolution of stratospheric processes, the height of the model top, gravity wave drag, interactive ozone etc.) that affect the dynamical fingerprints.

A few papers have discussed the impact of greenhouse gases and volcanoes on the Northern Hemisphere modes (i.e. Shindell et al., 1999; 2001; 2004), while multi-model ensemble sensitivity to ozone and greenhouse gas forcing was assessed in Miller et al., 2006. Iin the Southern Hemisphere around Antarctica. the combination of stratospheric ozone depletion and greenhouse gas changes was discussed in Shindell and Schmidt (2004).

Another topic of interest is examining the variability in short-term trends in global mean temperature, and specicifically the 'slowdown' in the rate of warming in the last 15 years. Two recent papers examined the role of volcanic and other forcings in combination with internal variability to explain recent changes (e.g. Santer et al, 2014; Schmidt et al., 2014).

Modelling Paleo-Climates

Past climates provide an assessment of model skill in in ways that can greatly add to their credibility to simulations of future climate (Schmidt (2010), Schmidt et al. (2014)).

Paleo-climate modelling involves at least two aspects, defining the relevant boundary conditions and ensuring that the models have sufficient scope to deal with the specifc scientific question. I have been involved particularly in defining the boundary conditions used for the simulations of the last millennium (850 CE to 1850 CE) (Schmidt et al., 2011; 2012).

In more general terms, we have worked on modelling approaches for understanding the climate of the Holocene (Schmidt et al, 2004, QSR). One focus I (along with colleagues Drew Shindell and David Rind) have is the role of natural forcing mechanisms, such as solar or volcanic variability, over the last few hundred years. In particular we looked at the so-called Little Ice Age, or more precisely the Maunder Minimum (at the end of the 17th century). We found that by reducing the solar forcing in line with estimates (Lean et al, 1997), we can get substantial regional changes in surface temperatures (particularly over the NH continents) even though the global change is relatively small. This then may be a plausible solution for discrepancies between Little Ice Age glacial advances, and minimal evidence for a large global cooling. The impact of volcanism over this same period cannot be neglected, and we found that both effects are likely to have been important, but each has a specific regional expression. A number of papers have now appeared that look at this issue from the data and modelling standpoints: Shindell et al. ( 2001; 2003; 2006).

Other impacts of changes in solar activity can be seen in the cosmogenic isotopes such as 14C, 7Be, and 10Be. Since these are the proxies that we use to determine solar activity in the past, understanding how that are impacted by climate or other changes is essential to appropriately interpreting them in the real world, for instance: Field et al (2006; 2009a; 2009b).

The Paleocene/Eocene Thermal Maximum (PETM) is another interesting candidate for seeing whether or not GCMs can replicate the sensitivity of the climate to forcings that happened (in this case) 55 million years ago. This global warming event is hypothesized to have been forced by massive releases of methane gas from frozen methane hydrate deposits on the sea floor. The methane and its oxidation product CO2 are both powerful greenhouse gases, but the relative importance of methane and CO2 is controlled by atmospheric chemistry. Squaring the circle of forcing, modelling and outcome for this event is the subject of Schmidt and Shindell (2003).

The PETM is but one example of the influence of atmospheric methane on climate (and vice versa). I wrote a article for La Recherche (a French popular science magazine) that discusses how methane went from obscurity 30 years ago to one of the most important subjects in climate science today. (The article is originally in French, but there is an English translation as well).

A very interesting period for modellers and paleo-oceanographers is the so-called 8.2 kyr event. This is the last abrupt climate change that occurred in the North Atlantic, and appears to have been co-incident with a sudden outburst event from Lake Agassiz - then the largest freshwater lake on the planet. This event is proving to be an excellent target for understanding the sensitivity of the ocean circulation to freshwater additions. Some explanation of why this may be so is explained in an editorial (Schmidt and LeGrande, 2005) and in some of our own modelling results: LeGrande et al (2006; 2008).

We continue to work on other climate periods, such as the Pliocene, the Last Glacial Maximum and millennial variability in the ice ages, as well as even deeper times, such as the Eocene or even Snowball Earth .

Please feel free to contact me for further information or reprints.

NASA Goddard Institute for Space Studies
Center for Climate Systems Research, Columbia University
2880 Broadway, New York, NY 10025 USA

Tel: (212) 678 5627
Email: Gavin.A.Schmidt-at-nasa.gov *

*Please note that emails sent to government addresses may be subject to disclosure under FOIA and that you should have no expectation of privacy. If you want to contact me in a non-official capacity, please do so via my columbia email. (Replace the -at- with the @ sign).