Downscaling IPCC control run and future scenario with focus on the Barents Sea

Abstract

Global atmosphere-ocean general circulation models are the tool by which projections for climate changes due to radiative forcing scenarios have been produced. Further, regional atmospheric downscaling of the global models may be applied in order to evaluate the details in, e.g., temperature and precipitation patterns. Similarly, detailed regional information is needed in order to assess the implications of future climate change for the marine ecosystems. However, regional results for climate change in the ocean are sparse. We present the results for the circulation and hydrography of the Barents Sea from the ocean component of two global models and from a corresponding pair of regional model configurations. The global models used are the GISS AOM and the NCAR CCSM3. The ROMS ocean model is used for the regional downscaling of these results (ROMS-G and ROMS-N configurations, respectively). This investigation was undertaken in order to shed light on two questions that are essential in the context of regional ocean projections: (1) How should a regional model be set up in order to take advantage of the results from global projections; (2) What limits to quality in the results of regional models are imposed by the quality of global models? We approached the first question by initializing the ocean model in the control simulation by a realistic ocean analysis and specifying air-sea fluxes according to the results from the global models. For the projection simulation, the global models’ oceanic anomalies from their control simulation results were added upon initialization. Regarding the second question, the present set of simulations includes regional downscalings of the present-day climate as well as projected climate change. Thus, we study separately how downscaling changes the results in the control climate case, and how scenario results are changed. For the present-day climate, we find that downscaling reduces the differences in the Barents Sea between the original global models. Furthermore, the downscaled results are closer to observations. On the other hand, the downscaled results from the scenario simulations are significantly different: while the heat transport into the Barents Sea and the salinity distribution change modestly from control to scenario with ROMS-G, in ROMS-N the heat transport is much larger in the scenario simulation, and the water masses become much less saline. The lack of robustness in the results from the scenario simulations leads us to conclude that the results for the regional oceanic response to changes in the radiative forcing depend on the choice of AOGCM and is not settled. Consequently, the effect of climate change on the marine ecosystem of the Barents Sea is anything but certain.

Appendix

The equations in this appendix are provided by Bentsen and Drange (2000). Bulk expressions relating turbulent fluxes of momentum and heat to measurable atmospheric variables (Smith et al. 1996)

$$ \tau=-\rho_{a}C_{D}Su, $$

(1)

$$ Q_{H}=\rho_{a}c_{pa}C_{H}S(T_{s}-\Theta), $$

(2)

$$ Q_{L}=\rho_{a}L_{e}C_{E}S(q_{s}-q), $$

(3)

where ρ _a is the air density; C _D , C _H , and C _E are the transfer coefficients for momentum, sensible heat, and latent heat, respectively; S is the average wind speed; u is the mean wind vector; Θ is the potential temperature; and q is the specific humidity, all measured at the reference height z _r . T _s and q _s are the temperature and specific humidity at sea surface, respectively.

If the turbulent fluxes of momentum, heat, and sea surface state are available from reanalysis or an atmosphere circulation model, the corresponding near surface atmospheric state can be estimated. This atmospheric state together with the sea surface state from the model is used to estimate the final fluxes to be applied. An iterative scheme is used to calculate the atmospheric state at height z _r , and first guesses must be made for the transfer coefficients, the gustiness, w _G , and air density. In the following, the subscript n denotes the iteration number, d denotes data from reanalysis or atmospheric model, and m denotes ocean model.

The mean wind speed vector u _n at z _r is solved from the bulk expression:

$$ \tau_{d}= -\rho^{n-1}_{a}C^{n-1}_{D,d}\sqrt{(u^{n})^{2}+(w^{n-1}_{G,d})^{2}}u^{n}, $$

(4)

and is given by

$$ u_{n}= \sqrt{ \frac{1}{2}\left(-(w^{n-1}_{G,d})^{2}+\sqrt{ (w^{n-1}_{G,d})^{4}+4\left(\frac{\tau_{d}}{\rho^{n-1}_{a}C^{n-1}_{D,d}}\right)^{2}} \right) }. $$

(5)

The average wind speed $S=\sqrt {u^{2}+{w_{G}^{2}}}$ at z _r is updated as

$$ S^{n}= \sqrt{(u^{n})^{2}+(w^{n-1}_{G,d})^{2}}. $$

(6)

Thereafter, the potential temperature and specific humidity at z _r are solved from the bulk expressions $Q_{H,d}=\rho ^{n-1}_{a}c_{pa}C^{n-1}_{H,d}S^{n}(T_{s,d}-\Theta ^{n})$ and

$Q_{L,d}=\rho ^{n-1}_{a}L_{e}C^{n-1}_{E,d}S^{n}(q_{s,d}-q^{n})$, respectively, and result in

$$ \Theta^{n}= T_{s,d}-\frac{Q_{H,d}}{\rho^{n-1}_{a}c_{pa}C^{n-1}_{H,d}S^{n}}, $$

(7)

$$ q^{n}= q_{s,d}-\frac{Q_{L,d}}{\rho^{n-1}_{a}L_{e}C^{n-1}_{E,d}S^{n}}. $$

(8)

The air density are finally updated by a standard equation of state for moist air (Gill 1982)

$$ {\rho^{n}_{a}}=\rho_{a}(\Theta^{n},q^{n},p_{s,d}) $$

(9)

where p _s,d is the surface pressure from the data. A new set of transfer coefficients C _D,m ,C _H,m ,a n d C _E,m and gustiness, w _G,m , are computed with the atmospheric state, consistent with the sea surface state represented by T _s,m and q _s,m . Using Eqs. 1–3, the turbulent fluxes to be used by the ocean model are:

$$ \tau_{m}=-{\rho^{n}_{a}}C_{D,m}S^{n}u^{n}, $$

(10)

$$ Q_{H,m}={\rho^{n}_{a}}c_{pa}C_{H,m}S^{n}(T_{s,m}-\Theta^{n}), $$

(11)

$$ Q_{L,m}={\rho^{n}_{a}}L_{e}C_{E,m}S^{n}(q_{s,m}-q^{n}). $$

(12)

For parameterization of net long-wave radiation at the sea surface, Q _l,m , the approach by Berliand and Berliand (1952) is used

$$\begin{array}{@{}rcl@{}} Q_{l,m}=4\varepsilon\sigma {T^{3}_{a}}(T_{s,m}-T_{a})+\varepsilon\sigma {T^{4}_{a}}\left(0.39-0.005\sqrt{e(T_{a})}\right)(1-\chi C^{2}), \end{array} $$

(13)

where ε is the emissivity of water, σ is the Stefann-Boltzmann constant, e is the water vapor pressure, and (1−χ C ²) is a cloud correction term. The air temperature at z _r =10 m is related to the estimated potential temperature as

$$ T_{a}=\Theta^{n}-0.0098z_{r} $$

(14)

To get consistent heat fluxes, the net long-wave radiation supplied by the reanalysis or atmospheric model, Q _l,d , is used

$$\begin{array}{@{}rcl@{}} Q_{l,d}=4\varepsilon\sigma {T^{3}_{a}}(T_{s,d}-T_{a})+\varepsilon\sigma {T^{4}_{a}}\left(0.39-0.005\sqrt{e(T_{a})}\right)(1-\chi C^{2}) \end{array} $$

(15)

By subtracting Eq. 15 from Eq. 13, the following expression for net long-wave radiation results

$$ Q_{l,m}=Q_{l,d}+4\varepsilon\sigma {T^{3}_{a}}(T_{s,m}-T_{s,d}). $$

(16)